Tourist Photos Research Articles

Visual Representations of Abandoned Industrial Spaces in Tourist Photos

A few decades ago, an enthusiastic researcher would have found relatively few data to analyze, looking for such information about the ruins of modernity as the structural consequences caused by rapid cycles of industrialization and desolation, development and depopulation, conflict and reconciliation. However, conducting the same search today reveals an extraordinary increase in academic and public interest in the ruins of the recent past and the associated abandoned places. The authors of this article join in the discussion and explore the images of deindustrialization posted and distributed in the online communities among abandoned sites enthusiasts. The aim of the research is to form an idea of “everyday life policy” about the abandoned industrial facilities popularized by amateur photographers. “Everyday life policy” is expressed through the signifying and conceptualizing the abandoned and ruined places when putting them in a close-up. The authors rely on the ideas of the architect and historian I. de Sola-Morales, who calls the places left by people “terrain vague”. Photographic images of the terrain vague spaces play an important role in conceptualizing them. In order to understand what meanings exactly shaped the public policy, the authors use the ethnographic method of immersion in the online VKontakte/Telegram communities of abandoned sites enthusiasts to discover the recurring iconographic elements of the derelict places. Deindustrialization in photographs is reflected through 1) loss of human; 2) violence of vegetation or nature agency; 3) artifacts and texts of the past or material objects agency. The authors discuss the similarities of the images taken by amateur photographers and those media clichés found in popular movies and video games. Photographs are a way of explaining space and spatial experience. Furthermore, like any other work of art, a photo conveys not only and not so much the actual characteristics of the depicted objects, thanks to which we can form an idea about them (in this case, about space), but also effects and experiences. Thus, a photograph becomes a tool that helps us form value judgments about the places we have seen.

ATiTHi: A Deep Learning Approach for Tourist Destination Classification using Hybrid Parametric Optimization

A picture is best way to explore the tourist destination by visual content. The content-based image classification of tourist destinations makes it possible to understand the tourism liking by providing a more satisfactory tour. It also provides an important reference for tourist destination marketing. To enhance the competitiveness of the tourism market in India, this research proposes an innovative tourist spot identification mechanism by identifying the content of significant numbers of tourist photos using convolutional neural network (CNN) approach. It overcomes the limitations of manual approaches by recognizing visual information in photos. In this study, six thousand photos from different tourist destinations of India were identified and categorized into six major categories to form a new dataset of Indian Trajectory. This research employed Transfer learning (TF) strategies which help to obtain a good performance measure with very small dataset for image classification.VGG-16, VGG-19, MobileNetV2, InceptionV3, ResNet-50 and AlexNet CNN model with pretrained weight from ImageNet dataset was used for initialization and then an adapted classifier was used to classify tourist destination images from the newly prepared dataset. Hybrid hyperparameter optimization employ to find out hyperparameter for proposed Atithi model which lead to more efficient model in classification. To analyse and compare the performance of the models, known performance indicators were selected. As compared to the AlexNet model (0.83), MobileNetV2(0.93), VGG-19(0.918), InceptionV3(0.89), ResNet-50(0.852) the VGG16 model has performed the best in terms of accuracy (0.95). These results show the effectiveness of the current model in tourist destination image classification.

Classification of Tourist Photos with Gorillas and Other Animals to Detect Distancing Violations

Many people insist on getting close to wildlife without realizing how harmful the consequences of their actions can be such as the death of the animals or even themselves. To decrease the number of such events, we aim to classify tourist photos of mountain gorillas, a critically endangered species, and other animals to detect violations derived from the distance maintained between the animals and the humans. We plan to implement object detection technology to locate the human and animal targets in the image and generate features. Then, image processing techniques are applied. We propose innovative models following multi-input logic which takes in image data and non-image features with ensemble model logic to perform the classification. We show that this model performs more accurately and has a relatively shorter run-time compared to other models when detecting violations in tourist photos with animals.

Tourism-supported working lands sustain a growing jaguar population in the Colombian Llanos

Understanding large carnivore demography on human-dominated lands is a priority to inform conservation strategies, yet few studies examine long-term trends. Jaguars (Panthera onca) are one such species whose population trends and survival rates remain unknown across working lands. We integrated nine years of camera trap data and tourist photos to estimate jaguar density, survival, abundance, and probability of tourist sightings on a working ranch and tourism destination in Colombia. We found that abundance increased from five individuals in 2014 to 28 in 2022, and density increased from 1.88 ± 0.87 per 100 km2 in 2014 to 3.80 ± 1.08 jaguars per 100 km2 in 2022. The probability of a tourist viewing a jaguar increased from 0% in 2014 to 40% in 2020 before the Covid-19 pandemic. Our results are the first robust estimates of jaguar survival and abundance on working lands. Our findings highlight the importance of productive lands for jaguar conservation and suggest that a tourism destination and working ranch can host an abundant population of jaguars when accompanied by conservation agreements and conflict interventions. Our analytical model that combines conventional data collection with tourist sightings can be applied to other species that are observed during tourism activities.Entender los patrones demográficos de los grandes carnívoros al interior de paisajes antrópicos es fundamental para el diseño de estrategias de conservación efectivas. En el Neotrópico, el jaguar (Panthera onca) es una de estas especies cuyas tendencias poblacionales y tasas de supervivencia en paisajes productivos son desconocidas. Para entender mejor estas dinámicas, integramos nueve años de fototrampeo junto a fotos de turistas para estimar la densidad, supervivencia, abundancia y probabilidad de avistamiento de esta especie en una finca ganadera y destino turístico en Colombia. Entre 2014 y 2022 encontramos que la abundancia incrementó de cinco a 28 individuos y la densidad de 1.88 ± 0.87 jaguares/ 100 km2 a 3.80 ± 1.08 jaguares/ 100 km2. La probabilidad de avistamiento por turistas aumentó de 0% en 2014 a 40% en 2020 antes de la pandemia del Covid-19. Nuestros resultados presentan las primeras estimaciones robustas de abundancia y supervivencia de este felino en paisajes antrópicos dónde el manejo de sistemas productivos combinados con turismo e intervenciones para la mitigación del conflicto puede albergar poblaciones abundantes de jaguares, demostrando su importancia para la conservación de esta especie. Nuestro modelo, al combinar datos convencionales con avistamientos, podría ser aplicado a otras especies observadas durante actividades turísticas.

Research on Spatio-Temporal Characteristics of Tourists' Landscape Perception and Emotional Experience by Using Photo Data Mining.

Mountainous scenic spots are important tourism resources, and the study of tourists' landscape perception and emotional preference when visiting them is beneficial to the management of scenic spots in order to improve the service quality and promote the protection, development, and utilization of scenic resources. In this paper, we use the location photo data of tourists at Huangshan Mountain to extract the visual semantic information of location photos, calculate the photo sentiment value, and mine the landscape perception and sentiment preference features of tourists using DeepSentiBank image recognition model and photo visual semantic quantification method. The results show the following: (1) Huangshan tourists mainly focus on nine types of photos, with the most attention paid to the category of mountain rock landscapes and the least attention paid to the category of animal landscapes. (2) In terms of spatial distribution, the landscape types of tourist photos show the spatial characteristics of "concentrated into a belt", "significant nucleus", and "fragmented distribution". The spatial variation of the emotional value of tourists' photos is significant, and the high values are mainly distributed at the entrances and exits, interchanges, and famous attractions. (3) On a temporal scale, the type of perception of the Huangshan location photograph landscape shows a significant imbalance. The emotional values of tourists' photos vary significantly, with a "slowly sloping straight line" type of emotional change on the seasonal scale, a "W" type of emotional change on the monthly scale, an "N" type of emotional change on the weekly scale, and an "M" type of emotional change on the hourly scale. This study attempts to explore the landscape perceptions and emotional preferences of tourists in mountainous scenic areas with new data and methods, aiming to promote the sustainable and high-quality development of mountainous scenic areas.

What attracts tourists to press the shutter in cultural heritage tourism? An analysis of visitor-employed photography and visual attributes: a case study on Japan’s Kairakuen Garden

ABSTRACT Tourism behaviour in cultural heritage gardens presents opportunities and challenges for sustainable management. Understanding visitor perceptions and assessments of visual resources in cultural heritage gardens are of great interest to heritage site managers. Using a case study of the Kairakuen Garden in Japan, we collected images (N = 430) and geographic data of tourist photos in the heritage garden through visitor-employed photography technology to analyse what hotspots attract tourists to take photos. We also evaluated the visual attributes of photo hotspots using a questionnaire. The results reveal that the most common elements in tourist images were plant landscapes and human-made structures and that the 12 photo hotspots of the Kairakuen Garden were placed along the officially recommended tour routes, indicating that tourists identify the officially approved scenery. Furthermore, there are four main hotspot areas; they received significant different visual attributes assessments in ‘scenic beauty’, ‘complexity’, ‘mystery’, and ‘diversity’. Based on these findings, it is possible to understand tourists’ visual perceptions and preferences and to make recommendations for the management of the Kairakuen Garden and other cultural heritage gardens.

1960~1980년대 제주도 관광이미지에 대한 서설적 연구

lbums and Jeju-do’s tourist brochures issued from 1960s to 1980s. It, therefore, examines how the visual typicality of Jeju-do has been created. The period suggested by this study was an inflection point in which the economy and culture of it were changed and the government-led tourist policy for transforming Jeju-do into a destination which is attractive enough to be called the ‘Oriental Hawaii’ was developed. The tourist image produced in the period was successful in promoting Jeju-do as an exotic island. The most prominent characteristics of such tourist photos, however, is that they continuously and repeatedly use the same material. Such images have formed the typicality of Jeju-do, as they have been actively used until a recent time in explaining Jeju-do. In conclusion, it would show how Jeju-do is visually perceived, as the tourist industry is changed, by analyzing the stereotyped tourist images of Jeju-do by each period, which are printed in Jeju-do’s tourist brochures and tourist photo albums. It aims to describe them as a field in which images can be exchanged, beyond visual materials for tourism.

Contour information regularized tensor ring completion for realistic image restoration

Tensor completion has gained considerable research interest in recent years and has been frequently applied to image restoration. This type of method basically employs the low-rank nature of images, implicitly requiring that the whole picture is of globally consistent features. As a result, existing tensor completion algorithms often give reasonably good performance if the target image has only random pixel-level missing. Unfortunately, pixel-level missing is very rare in practice and it is often wanted to restore an image with irregular hole-shaped missing, such as removing electricity poles from landscape photos or irrelevant people from tourist photos. This task is extremely difficult for traditional low-rank based tensor completion methods. To overcome this drawback, a Contour Information regularized Tensor RIng Completion (CITRIC) method is proposed for practical image restoration. Meanwhile, the contour information regularization is used to capture significant local features, whereas the low-rank tensor ring structure is utilized to capture as much global information as possible. The alternating direction method of multipliers (ADMM) is adopted to optimize the cost function. Extensive experimental results using real-world images show that CITRIC is more practical than existing methods and can restore real-world images with irregular hole-shaped missing.

Automatic Classification of Photos by Tourist Attractions Using Deep Learning Model and Image Feature Vector Clustering

With the rise of social media platforms, tourists tend to share their experiences in the form of texts, photos, and videos on social media. These user-generated contents (UGC) play an important role in shaping tourism destination images (TDI) and directly affect the decision-making process of tourists. Among UGCs, photos represent tourists’ visual preferences for a specific area. Paying attention to the value of photos, several studies have attempted to analyze them using deep learning technology. However, the research methods that analyze tourism photos using recent deep learning technology have a limitation in that they cannot properly classify unique photos appearing in specific tourist attractions with predetermined photo categories such as Places365 or ImageNet dataset or it takes a lot of time and effort to build a separate training dataset to train the model and to generate a tourism photo classification category according to a specific tourist destination. The purpose of this study is to propose a method of automatically classifying tourist photos by tourist attractions by applying the methods of the image feature vector clustering and the deep learning model. To this end, first, we collected photos attached to reviews posted by foreign tourists on TripAdvisor. Second, we embedded individual images as 512-dimensional feature vectors using the VGG16 network pre-trained with Places365 and reduced them to two dimensions with t-SNE(t-Distributed Stochastic Neighbor Embedding). Then, clusters were extracted through HDBSCAN(Hierarchical Clustering and Density-Based Spatial Clustering of Applications with Noise) analysis and set as a regional image category. Finally, the Siamese Network was applied to remove noise photos within the cluster and classify photos according to the category. In addition, this study attempts to confirm the validity of the proposed method by applying it to two representative tourist attractions such as ‘Gyeongbokgung Palace’ and ‘Insadong’ in Seoul. As a result, it was possible to identify which visual elements of tourist attractions are attractive to tourists. This method has the advantages in that it is not necessary to create a classification category in advance, it is possible to flexibly extract categories for each tourist destination, and it is able to improve classification performance even with a rather small volume of a dataset.

Do Visitors Visit What the Tours Industry Supplies? Sightseeing Tours Versus First-timers’ Photos

ABSTRACT Tourist behaviour within destinations is a research topic of increasing interest. The most relevant approaches focus on demand and are aimed at detecting factors affecting travel patterns, or revealing the mobility flows shaping tourism destinations. This paper adds another layer to this stream of research by including the supply-side, contrasting it with the demand-side. It aims to detect potential market opportunities by overlapping the geographical distribution of geolocated user-generated photos from social media taken by first time visitors to Cartagena, Colombia, versus the attractions offered in city tours. Mixed-method approaches are used to analyse the association between both datasets, including spatial analysis, correlation and cluster analysis. The results show a partial overlap in the correlation between attractions included in city tours and geotagged tourist photos. This opens up a debate on the need for tours to adapt their products to how visitors consume the city's attractions and spaces of interest.

Characterizing Tourism Destination Image Using Photos’ Visual Content

“A picture is worth a thousand words”. Analysis of the visual content of tourist photos is an effective way to explore the image of tourist destinations. With the development of computer deep learning and big data mining technology, identifying the content of massive numbers of tourist photos by convolutional neural network (CNN) approaches breaks through the limitations of manual approaches of identifying photos’ visual information, e.g., small sample size, complex identification process, and results deviation. In this study, 531,629 travel photos of Jiangxi were identified as 365 scenes through deep learning technology. Through the latent Dirichlet allocation (LDA) model, five major tourism topics are found and visualized by map. Then, we explored the spatial and temporal distribution characteristics of different tourism scenes based on hot spot analysis technology and the seasonal evaluation index. Our research shows that the visual content mining on travel photos makes it possible to understand the tourism destination image and to reveal the temporal and spatial heterogeneity of the image, thereby providing an important reference for tourism marketing.

Learning patterns of tourist movement and photography from geotagged photos at archaeological heritage sites in Cuzco, Peru

The popularity of media sharing platforms in recent decades has provided an abundance of open source data that remains underutilized by heritage scholars. By pairing geotagged internet photographs with machine learning and computer vision algorithms, we build upon the current theoretical discourse of anthropology associated with visuality and heritage tourism to identify travel patterns across a known archaeological heritage circuit, and quantify visual culture and experiences in Cuzco, Peru. Leveraging large-scale in-the-wild tourist photos, our goals are to (1) understand how the intensification of tourism intersects with heritage regulations and social media, aiding in the articulation of travel patterns across Cuzco's heritage landscape; and to (2) assess how aesthetic preferences and visuality become entangled with the rapidly evolving expectations of tourists, whose travel narratives are curated on social media and grounded in historic site representations.

The Impact of Instagram account @visitbogor and uploads of nature tourism photos towards the Visiting Interests of Its Followers (Empirical Study on Visit Interests in natural tourism of Bogor city)

This research is conducted with the aim of knowing the impact of the Instagram account @visitbogor to the interest in visiting the followers, knowing the effects of uploading natural travel photos on a visiting interest of followers, knowing the impact of Instagram account @visitbogor and uploading of natural travel photos to the interest of visiting followers. This research samples a total of 400 respondents with purposive sampling techniques filtered on the basis of certain criteria. The study uses the S-O-R theory. The data analysis methods in this study used multiple linear regression analyses and hypothesis testing using the T test and F test with the help of SPSS version 22. Acquired regression coefficient value Y = 2.160 + 0.163 X1 + 0.720 X2, relationship level correlation value is said to be strong because it exists at a 0.60-0,799 interval of 0.730 with a percentage of 73% coefficient of determination value of 0.533 which means that 53.3% of visiting interest in followers is influenced by the Instagram account is presented @visitbogor and upload of nature tourism photos with H1 received based on the results of Thitung (2.195) Ttabel (1.966) on a 0.05. H2 received according to Thitung (20.230) Ttabel (1.966) on sig α = 5% with sig value of 0.000 0.05. H3 is received based on the result of Fcalculate (226.196) Ftabel (3.86) on a sig α = 5% with a sig value of 0.000 0.05. The results showed a significant influence between the variables of the Instagram account's cast @visitbogor and the uploading of natural tourist photos of the followers ' visiting interests.

Discovering the tourists' behaviors and perceptions in a tourism destination by analyzing photos' visual content with a computer deep learning model: The case of Beijing

Visual content analysis of tourist photos is an effective way to excavate tourist behavior and explore tourists' cognition in the tourism destination. With the development of computer deep learning and big data mining technology, identifying the content of massive numbers of tourist photos by Artificial Intelligence (AI) approaches breaks through the limitations of manual approaches of identifying photos' visual information, e.g. small sample size, complex identification process and results deviation. In this study, 35,356 Flickr tourists' photos in Beijing were identified into 103 scenes by computer deep learning technology. Comparison through statistical analysis for behaviors and perceptions of tourists from different continents and countries was conducted. Tourists' cognitive maps with different perceptual themes were visualized according to photos' geographical information by ArcGIS. The field of how to apply AI technology into tourism destination research was explored and extended by this trial study.

Journal entries, maps, and photos help ecologists reconstruct ecosystems of the past

Ilka Feller has a penchant for mangrove hunting. Since the early 2000s, Feller, an ecologist at the Smithsonian Environmental Research Center, has periodically gone road-tripping in search of the northernmost mangrove tree in Florida. The red mangrove, Rhizophora mangle , has draping roots that hold fast in the tide. But all three of Florida’s mangrove species anchor themselves and whole ecosystems—protecting the shoreline from erosion, forming nurseries for fish, and sequestering carbon. Ecologist Ilka Feller used archival records to investigate whether mangroves in Florida, like these in Indian River Lagoon, are expanding into new territories or returning to a historical range. Image credit: Ilka Feller. Globally, mangroves are threatened by coastal development and shrimp farming. “More than half of the world’s mangroves have already been destroyed,” says Feller. Yet in Florida, Feller kept finding new mangroves farther and farther north, “solitary plants that are out there in this forever saltmarsh.” As part of a 2014 study, Feller’s then postdoc, Kyle Cavanaugh, analyzed satellite images taken from 1984 to 2011 and saw that mangroves were indeed advancing northward (1). The spread was associated with a reduced frequency of extreme cold events. Climate change, it seemed, was replacing marshland, an important temperate ecosystem, with mangroves, an important subtropical and tropical one. But had they really revealed a trend pointing in one direction, or was the recent expansion of mangroves part of a continual cycle of mangrove and saltmarsh replacing one another? Feller needed more than just conventional scientific data to find out. So she dug into Florida’s past using tools more typical of historians. She found mangroves mentioned in the texts of famed naturalist John Muir, uncovered data in tourist photos, and parsed the writings of war surgeons and beekeepers. Feller and a growing group of ecologists are piecing together historical records …

Wildlife tourism in Latin America: taxonomy and conservation status

We provide an initial insight into the extent, occurrence and characteristics of wildlife tourism involving close interactions with free-ranging, non-domesticated, animals outside of formal captive environments across Latin America. Using information provided online via TripAdvisor, we found this type of tourism was occurring across the region (advertised on 249 wildlife tourist attraction webpages across 21 countries) and involved a diverse range of wild animals (73 species, including 19 currently considered as threatened by the IUCN). Opportunities for direct contact with wild animals were particularly prevalent (54% of all surveyed webpages). Despite the potential economic benefits, studies have indicated that these types of ecotourism are potentially having net negative impacts on wildlife conservation and welfare. Mammals classified as Least Concern featured most commonly in tourist photos, but our analyses suggest that mammals and species classified as Vulnerable on the IUCN Red List were most likely to occur in these types of wildlife tourist attractions (WTAs). Amphibians and species classified on the IUCN Red List as Data Deficient or Critically Endangered were least likely. Given the growing nature of the wildlife tourism sector, we provide recommendations to help effectively balance and manage wider wildlife protection goals and growing tourist interest in wildlife.

Monument Valley, Instagram, and the Closed Circle of Representation

Monument Valley, Instagram, and the Closed Circle of Representation

Guest Editors' Introduction: Special Section on Connected Multimedia

Social media has received extensive attention recently and has become a very popular research area due to its wide spectrum of applications. We note that even though the whole area of social media is very popular in the literature, there are a group of research issues that are related to the social-cultural constraints in the social media study that have not yet received sufficient attention. In this context, we group all these issues together under the umbrella of a new sub-area of social media that we call connected multimedia . Consequently, by connected multimedia, we mean the study of the social and technical interactions among users, multimedia data, and devices across cultures and the explicit exploitation of cultural differences. Hence, connected multimedia involves the three elements – the users, the multimedia data, and the devices – with two perspectives – the social focus and the cultural focus. In short, connected multimedia is about multimedia content and connection across community and cultural boundaries. In comparison with those existing research areas including social media as its super-area and human centered computing, we here emphasize that connected multimedia pays more attention to the cultural difference. The definition of the social side is broader than just national cultures; it possibly includes cultures of groups, disciplines, organizations, communities, ethnicities, religions, and nations. This emphasis distinguishes connected multimedia from all other existing areas such as social media and cross-media, which may either claim to include some of these aspects, among many others, or have different foci. This special section is organized following the successful first two editions of the workshop on the newly emerged theme of connected multimedia held in Hangzhou, China, in October of 2009 and in Florence, Italy, in October, 2010. After two rounds of rigorous reviews for all the submissions in response to the CFP of this special section, we have finally selected three papers to be included in this special section on connected multimedia. The paper “Association of Moving Objects across Visual Sensor Networks” by M.J. Mirza and N. Anjum presents an inter-camera trajectory association algorithm for partially overlapping visual sensor networks; the paper addresses issues related to trajectory extraction, representation, and association. The paper “Faceted Subtopic Retrieval: Exploiting the Topic Hierarchy via a Multi-Modal Framework” by J. Sang and C. Xu presents a new framework for multi-modal analysis and retrieval called faceted subtopic retrieval that is tailored to complex queries to the social media data on the Web concerning political and social events or issues. A LDA-like model is developed to exploit the intrinsic topic hierarchy inside the retrieved data. The paper “Tour Route Recommendation Begins with Multimodal Classification” by X. Chen and Q. Wang addresses the problem of location estimation for tourist photos; the paper proposes a solution that begins with classification and exploits explicitly the textual, temporal, geographic, and visual information together for a tour route recommendation. Overall, all the three papers address the theory and application case studies on the general topic of connected multimedia. While this editorial is co-authored by the guest-editors who also organized the two editions of the workshop on the same topic that actually led to this special section, many people have contributed the ideas that have finally led to the development of this topic of connected multimedia in the literature. Specifically, Noshir Contractor, Alan Hanjalic, Alexander G. Hauptmann, Xian-Sheng Hua, Alejandro (Alex) Jaimes, Ivan Ivanov, Michael S. Lew, Wanqing Li, Ching-Yung Ling, Alexander C. Loui, Jiebo Luo, Michael W. Macy, Nicu Sebe, Qi Tian, Yonghong Tian, Vincent S. Tseng, Qing Wang, Changsheng Xu, Huimin Yu, and Shiwen Yu deserve the credit for contributing their ideas to the development of the literature on this topic. Finally, we would like to thank Dr. Jie Yang, US NSF Program Manager, for the support to the development of this effort and Dr. Jiebo Luo, the EIC of Journal of Multimedia, for the support to publishing this special section. We also acknowledge US NSF (through grant IIS-0956924), Zhejiang University College of Computer Science and Technology, Microsoft Research, and ACM for sponsoring the two editions of the workshop on connected multimedia that led to this special section. This effort of editing this special section is also supported in part by US NSF (IIS-0812114, CCF-1017828) and National Basic Research Program of China (2012CB316400).

Tour Route Recommendation Begins with Multimodal Classification

Location estimation of tourist photos by classifi­cation is challenging due to the unstable and less-discriminative features of photos taken in each city. In this paper, we originally deal with this issue in an alternative way, which begins with but is not limited to classification. We explore textual, temporal, geographic as well as visual information to make tour route recommendation. Given a query photo, we recommend a city by first classifying the photo to get scores that indicate its similarities with photos from each city, and then evaluating the attractiveness of each city by modeling its hotness potential. We do not aim at finding out where the photo was taken exactly; instead, we combine the similarity and hotness potentials according to the user’s preference and recommend the city that has the highest combination value. Then, we suggest the next city by further modeling the correlation and interaction between cities pairwise with two potentials, i.e., proximity and co-visitedness. Applying the greedy algorithm, we recommend one city at a time and eventually generate a tour route consist­ing of all the recommended cities in order. Furthermore, in order to show different visual and cultural character­istics across cities, we classify photos of each city into four categories, i.e., food, landscape, man-made and person. In experiments, we collected a database containing 41792 photos of 35 important cities along the silkroad and provided a query sample for tour route recommendation. Experimental results have shown the effectiveness and reliability of our recommendation model.

Re-Rereading Camera Lucida

... those who fail to re-read are obliged to read the same story everywhere. Roland Barthes, S/Z Artist and critic Victor Burgin uses the above quotation to open his essay Rereading Camera Lucida, critical examination of Roland Barthes's theoretical current throughout Camera Lucida: Reflections in Photography (1980), Image-Music-Text (1977), and Mythologies (1957). first time I read Camera Lucida I was on the train from Cusco to Machu Picchu, Peru, which travels alongside the Urubamba River in the Sacred Valley of the Incas in the Andes. At that moment, I was interested in photography's role in objectifying ruin and objectifying (a) people. In my rereading with Burgin, I found myself not only questioning my role but also reconsidering the phenomenology of my interpretation. [ILLUSTRATION OMITTED] As I watched others in the process, I lost all desire to capture any of the scenery around me. I'd had enough of the local women's persistent pressure: Foto, miss! Miss, foto. I did not want to pose with my family for cliche tourist photos, but it was impossible to disregard the occasion. As Barthes writes, did I care about the rules of composition of the photographic landscape, or, at the other end, about the Photograph as family rite? (1) like Barthes, I could not dismiss the intrinsic personal value of photography or retreat to simplistic sociological critique of tourist photography. Barthes says it best: Yet I persisted; another, louder voice urged me to dismiss such sociological commentary; looking at certain photographs, I wanted to be primitive, without culture. (2) At these words I paused. What did he mean by this? And was it appropriate? As I gazed out the train window, peasant children looked up from their work in the fields to wave as we passed them in floating flash. Barthes writes of subjective discomfort, being torn between two languages, one expressive, the other critical, and the effort to dismiss systematic reduction of his critical language to particular discourse such as semiology, sociology, or psychoanalysis. (3) This burden is obligatory to the artist, but what of the discomfort of the human being objectified as observed subject? observed might convey something through gesture or posture, but his fated objecthood inevitably undermines his critical capacity. object is never the Operator or the Spectator. To Barthes, the experience of being photographed involves sensation of inauthenticity, characterized as a subtle moment when, to tell the truth, I am neither subject nor object but subject who feels he is becoming an object: I then experience micro-version of death (of parenthesis): I am truly becoming specter. (4) By this account, the folk-dressed natives are corpses of their true beings while, to the tourist, they embody the original, unadulterated, and primitive. Their colorful dress, weathered skin, and dark hair beg objectification, but this palpability is beyond superficial. It is super-authentic. Their appearance is never for itself but for authenticity's sake. Dressed for admiration like dolls in cabinet, they become lifeless--or as Barthes suggests in consideration of photography's history, they become museum objects. (5) Their objectification by the tourist's photograph is, if not micro-death, an artfully subtle life-removal. Burgin intentionally rereads Barthes intertextually to delineate the structural motifs throughout his texts. (6) Barthes's early structuralism unequivocally reveals the binary distinction (in photography and literature) of denotation and connotation--the literal and the symbolic meanings. In his essays The Photographic Message (1961) and Rhetoric of the Image (1964) Barthes demonstrates the distinct but operationally related roles of the signifier. Their relationship, he contends, is paradoxical insofar as the literal image exist on its own and thus cannot be substantial but only relational. …