Large Photo Collections Research Articles

Geogebra in Real-Life: Important Tips to Support Student Creative Thinking in Mathematics

Creativity is an important personality trait to use in everyday life. This allows us to be flexible when facing real life situations. Mathematics education should be seen as an opportunity for creativity development, even though creativity is not traditionally associated with mathematics. One of the goals of education at every level is to encourage students to think creatively, think logically, and be able to solve problems. This study aimed to provide important tips to support the student creative thinking when studying mathematics. This study also described activities outside the classroom that could develop the student creativity related to geometry. The methods used were surveys, interviews, and classroom and outdoor observations. We used brainstorming in mathematics education. The results of the research during the survey were visible. Researchers collaborated with students aged 19 to 21 years. We asked them to search for “any geometry” and take pictures of interesting objects while they were doing the activity. Next, students created ideas for geometry assignments based on the photos they took. The large collection of photos collected represented the basis for creating mathematical problems without any specific steps related to geometric objects. These open problems could be solved by students. In conclusion, Geogebra could encourage the student creative thinking by providing the opportunity to experiment with their own ideas and find solutions to mathematical problems.

‘No trash – do not touch’: Handwritten textual objects at a construction site

Drawing on social semiotics and geosemiotics, this paper analyses multimodal texts written on different surfaces at a construction site. The analysis is based on longitudinal ethnographic work and a large collection of photos of handwritten texts that involve verbal language and other semiotic elements such as drawings or symbols. The analysis focuses on the multimodal design of the texts, their spatio-temporal contexts, and their temporal trajectories connected to the progression of the construction work. The analysis contributes to the existing research on language practices in manual work by providing new understanding of multimodal texts that are integral parts of construction workers’ day-to-day language use. The analysis also speaks to the importance of longitudinal and visually-based research designs in analyzing the language practices of manual and physical work contexts.

Publishing historical air photos in Alma Digital

Air photos have been used to support GIS-related research projects at the University of Ottawa, particularly in the field of climate science. The University of Ottawa Library houses a large collection of historical paper air photos and have recently increased efforts to further digitize this collection. Traditionally, the photographs have been searchable through a custom ArcGIS Online Web App, but this standalone portal has been limited in the accessibility and discoverability of these resources. In 2021, the University of Ottawa Library implemented Alma Digital as its institutional DAMS, which allowed its digital assets to be searchable directly within the library catalog, Omni. Following the implementation of this DAMS, close to 1,000 of the Library’s digital air photo collection were added to this platform. This has provided a new discovery portal for these digital air photos. Work to improve the discoverability, organization, and description of these resources, as well as numerous digitization projects, is still ongoing.

Snap Decisions: Assessing Participation and Data Quality in a Citizen Science Program Using Repeat Photography

Photo-point monitoring through repeat photography allows assessment of long-term ecosystem changes, and photos may be collected using citizen science methods. Such efforts can generate large photo collections, but are susceptible to varying participation and data quality. To date, there have been few assessments of the success of citizen science projects using repeat photography methods in meeting their objectives. We report on the success of the PhotoMon Project, a photo-point monitoring program at Pinery Provincial Park, Canada, at meeting its primary goals of affordably collecting seasonal reference photographs of significant ecosystems within the park, while providing a stewardship opportunity for park visitors. We investigated how the quantity of submitted photos varied over time (quantity), and how closely those photos matched the suite of criteria of the PhotoMon Project (quality). Photo submissions occurred year-round and at all sites, although a low proportion of park visitors participated in the program. Photo quantity varied among sites and seasonally, reaching a low during the winter, but with proportional participation in the project lowest in summer. Photo quality was consistent year-round, with most photos meeting most program criteria. Common issues with photo quality included photo lighting and orientation. We conclude that the program met its scientific goal of compiling seasonal reference photos, but that comparatively few park visitors engage in the program. We suggest changes to increase visitor motivation to participate, but recognize that these may compromise the program’s current affordability and ease of management.

Luxury Images on Instagram : Computational Analysis of Images Posted by Luxury Fashion Brands

The main purpose of this research is to explore what makes “good” luxury fashion images on Instagram and how Instagrammers respond to those images. A quantitative analysis of a large collection of fashion photos posted by notable luxury fashion brands was conducted to identify specific patterns in these images based on four important visual content variables: use of a brand name, brand logo, text, and hashtag. This study also examined how customer engagement levels vary depending on each visual content variable. Several interesting findings emerged from this study: (1) featuring a brand name was positively related to viewers’ engagement level in terms of the number of likes and comments, while the size of the brand name was negatively related to the number of likes; (2) featuring a brand logo was also positively related to viewers’ engagement level; and (3) the use of text and a hashtag was positively related to viewers’ engagement level, while sharing promotional information was likely to prompt a lower level of interaction.

Thing Constellation Visualizer: Exploring Emergent Relationships of Everyday Objects

Designing future IoT ecosystems requires new approaches and perspectives to understand everyday practices. While researchers recognize the importance of understanding social aspects of everyday objects, limited studies have explored the possibilities of combining data-driven patterns with human interpretations to investigate emergent relationships among objects. This work presents Thing Constellation Visualizer (thingCV), a novel interactive tool for visualizing the social network of objects based on their co-occurrence as computed from a large collection of photos. ThingCV enables perspective-changing design explorations over the network of objects with scalable links. Two exploratory workshops were conducted to investigate how designers navigate and make sense of a network of objects through thingCV. The results of eight participants showed that designers were actively engaged in identifying interesting objects and their associated clusters of related objects. The designers projected social qualities onto the identified objects and their communities. Furthermore, the designers changed their perspectives to revisit familiar contexts and to generate new insights through the exploration process. This work contributes a novel approach to combining data-driven models with designerly interpretations of thing constellation towards More-Than Human-Centred Design of IoT ecosystems.

Ethiopian Refugees in Kenya c.1937–1941: A Note on the Photo Collection at the Kenyan National Archive

The Kenya National Archives in Nairobi hosts a large collection of photos of British refugee camps taken during the Italian occupation of Ethiopia. This brief overview of the collection does not endeavour to analyse this little-known aspect of Ethiopian history but to introduce the photo collection as a possible tool to assess the history of migration and refuge in this part of the world.

“Other Turkestan” by Senator Konstantin Palen and engineer Nikolay Shchapov

This article is devoted to two practically unknown photo collections that visualize Turkestan at the beginning of the twentieth century. The first, stored in the Russian State Historical Archive (RGIA) and consisting of 4 photo albums, was collected during the Senate audit of the Turkestan Territory under the leadership of Count K. K. Palen in 1908—1909. The second collection of photographs, shot in 1911—1913 in Turkestan, is part of a large and diverse personal photo collection of hydrologist engineer N. M. Shchapov, stored in the Central State Archive of Moscow, the Center for the Storage of Electronic and Audiovisual Documents (TsGAM TsKHEAVD). Photocollections of the Senate audit (under the leadership of K. K. Palen) and hydro engineer N. M. Schapov, in our opinion, can be considered a visualization of modernization in Turkestan and a manifestation at the general imperial level of the appearance of another, industrial, Turkestan.

Investigating the Complexity of Spatial Interactions between Different Administrative Units in China Using Flickr Data

Location-based social media have facilitated us to bridge the gap between virtual and physical worlds through the exploration of human online dynamics from a geographic perspective. This study uses a large collection of geotagged photos from Flickr to investigate the complexity of spatial interactions at the country level. We adopted three levels of administrative divisions in mainland China—province, city, and county—as basic geographic units and established three types of topology—province–province network, city–city network, and county–county network—from the extracted user movement trajectories. We conducted the scaling analysis based on heavy-tailed distribution statistics including power law exponents, goodness of fit index, and ht-index, by which we characterized a great complexity of the trajectory lengths, spatial distribution of geotagged photos, and the related metrics of built networks. The great complexity indicates the highly imbalanced ratio of populated-to-unpopulated areas or large-to-small flows between areas. More interestingly, all power law exponents were around 2 for the networks at various spatial and temporal scales. Such a recurrence of scaling statistics at multiple resolutions can be regarded a statistical self-similarity and could thus help us to reveal the fractal nature of human mobility patterns.

Generating visual story graphs with application to photo album summarization

Making sense of ever-growing amount of visual data available on the web is difficult, especially when considered in an unsupervised manner. As a step towards this goal, this study tackles a relatively less explored topic of generating structured summaries of large photo collections. Our framework relies on the notion of a story graph which captures the main narratives in the data and their relationships based on their visual, textual and spatio-temporal features. Its output is a directed graph with a set of possibly intersecting paths. Our proposed approach identifies coherent visual storylines and exploits sub-modularity to select a subset of these lines which covers the general narrative at most. Our experimental analysis reveals that extracted story graphs allow for obtaining better results when utilized as priors for photo album summarization. Moreover, our user studies show that our approach delivers better performance on next image prediction and coverage tasks than the state-of-the-art.

Automated Bird Counting with Deep Learning for Regional Bird Distribution Mapping.

Simple SummaryTo detect changes in migrating bird populations that are usually gradual, regular counts of the flocks should be carried out. This is vital for giving more precise management decisions and taking preventive actions when necessary. Traditional counting methods are widely used. However, these methods can be expensive, time-consuming, and highly dependent on the mental and physical status of the observer and environmental factors. Taking these uncertainties into account, we aimed at taking the advantage of the advances in the artificial intelligence (AI) field for a more standardized counting action. The study has been practically initiated 10 years ago by beginning to take photos on a yearly basis in predefined regions of Turkey. After a large collection of bird photos had been gathered, we predicted the bird counts in photo locations from images by making strong use of AI. Finally, we used these counts to produce several bird distribution maps for further analysis. Our results showed the potential of learning computers in support of real-world bird monitoring applications.A challenging problem in the field of avian ecology is deriving information on bird population movement trends. This necessitates the regular counting of birds which is usually not an easily-achievable task. A promising attempt towards solving the bird counting problem in a more consistent and fast way is to predict the number of birds in different regions from their photos. For this purpose, we exploit the ability of computers to learn from past data through deep learning which has been a leading sub-field of AI for image understanding. Our data source is a collection of on-ground photos taken during our long run of birding activity. We employ several state-of-the-art generic object-detection algorithms to learn to detect birds, each being a member of one of the 38 identified species, in natural scenes. The experiments revealed that computer-aided counting outperformed the manual counting with respect to both accuracy and time. As a real-world application of image-based bird counting, we prepared the spatial bird order distribution and species diversity maps of Turkey by utilizing the geographic information system (GIS) technology. Our results suggested that deep learning can assist humans in bird monitoring activities and increase citizen scientists’ participation in large-scale bird surveys.

Whom to Follow? A Comparison of Walking Routes Computed Based on Social Media Photos from Different Types of Contributors

Abstract. Since many tourists share the photos they take on social media channels, large collections of tourist attraction photos are easily accessible online. Recent research has dealt with identifying popular places from these photos, as well as computing city tourism routes based on these photo collections. Although current approaches show great potential, many tourism attractions suffer from being overrun by tourists, not least because many tourists are aware of only a few tourism hot spots that are trending. In the worst case, automatic city route recommendations based on social media photos will intensify this issue and disappoint tourists who seek individual experiences. In the best case, however, if individual preferences are appropriately incorporated into the route planning algorithm, more personalized route recommendations will be achieved. In this paper, we suggest distinguishing two different types of photo contributors, namely: first-time visitors who are usually tourists who "follow the crowd" (e.g., to visit the top tourist attractions), and repeated visitors who are usually locals who "don’t follow the crowd" (e.g., to visit photogenic yet less well-known places). This categorization allows the user to decide how to trade the one objective off against the other. We present a novel method based on a classification of photographers into locals and tourists, and show how to incorporate this information into an algorithmic routing framework based on the Orienteering Problem approach. In detailed experiments we analyze how choosing the parameter that models the trade-off between both objectives influences the optimal route found by the algorithm, designed to serve the user’s travel objective and preferences in terms of visited attraction types.

Smartphone picture organization: A hierarchical approach

We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization.

Face Match for Family Reunification

Despite the many advances in face recognition technology, practical face detection and matching for unconstrained images remain challenging. A real-world Face Image Retrieval (FIR) system is described in this paper. It is based on optimally weighted image descriptor ensemble utilized in single-image-per-person (SIPP) approach that works with large unconstrained digital photo collections. The described visual search can be deployed in many applications, e.g. person location in post-disaster scenarios, helping families reunite quicker. It provides efficient means for face detection, matching and annotation, working with images of variable quality, requiring no time-consuming training, yet showing commercial performance levels.

From PhotoWork to PhotoUse: exploring personal digital photo activities

ABSTRACTPeople accumulate large collections of digital photos, which they use for individual, social, and utilitarian purposes. In order to provide suitable technologies for enjoying our expanding photo collections, it is essential to understand how and to what purpose these collections are used. Contextual interviews with 12 participants in their homes explored the use of digital photos, incorporating new photo activities that are offered by new technologies. Based on the qualitative analysis of the collected data, we give an overview of current photo activities, which we term PhotoUse. We introduce a model of PhotoUse, which emphasises the purpose of photo activities rather than the tools to support them. We argue for the use of our model to design tools to support the user’s individual and social goals pertaining to PhotoUse.

Adaptive 3D Face Reconstruction from Unconstrained Photo Collections.

Given a photo collection of "unconstrained" face images of one individual captured under a variety of unknown pose, expression, and illumination conditions, this paper presents a method for reconstructing a 3D face surface model of the individual along with albedo information. Unlike prior work on face reconstruction that requires large photo collections, we formulate an approach to adapt to photo collections with a high diversity in both the number of images and the image quality. To achieve this, we incorporate prior knowledge about face shape by fitting a 3D morphable model to form a personalized template, following by using a novel photometric stereo formulation to complete the fine details, under a coarse-to-fine scheme. Our scheme incorporates a structural similarity-based local selection step to help identify a common expression for reconstruction while discarding occluded portions of faces. The evaluation of reconstruction performance is through a novel quality measure, in the absence of ground truth 3D scans. Superior large-scale experimental results are reported on synthetic, Internet, and personal photo collections.

BUILDUP: interactive creation of urban scenes from large photo collections

We propose a system for creating images of urban scenes composed of the large structures typical in such environments. Our system provides the user with a precomputed library of image-based 3D objects, such as roads, sidewalks and buildings, obtained from a large collection of photographs. When the user picks the 3D location of a new object to insert, the system retrieves objects that have all the required properties (location, orientation and lighting). Then, the user interface guides the user to add more objects enabling non-experts to make a new composition in a fast and intuitive way. Unlike prior work, the entire image composition process is done in the 3D space of the scene, therefore inconsistent scale or perspective distortion does not arise, and occlusions are properly handled.

Topic Modeling Based Image Clustering by Events in Social Media

Social event detection in large photo collections is very challenging and multimodal clustering is an effective methodology to deal with the problem. Geographic information is important in event detection. This paper proposed a topic model based approach to estimate the missing geographic information for photos. The approach utilizes a supervised multimodal topic model to estimate the joint distribution of time, geographic, content, and attached textual information. Then we annotate the missing geographic photos with a predicted geographic coordinate. Experimental results indicate that the clustering performance improved by annotated geographic information.

Object-aware image thumbnailing using image classification and enhanced detection of ROI

Thumbnail images are used to display a large collection of photos in various digital devices. It aims for people to browse and search the image collection effectively. The provided thumbnail images...

Time-lapse mining from internet photos

We introduce an approach for synthesizing time-lapse videos of popular landmarks from large community photo collections. The approach is completely automated and leverages the vast quantity of photos available online. First, we cluster 86 million photos into landmarks and popular viewpoints. Then, we sort the photos by date and warp each photo onto a common viewpoint. Finally, we stabilize the appearance of the sequence to compensate for lighting effects and minimize flicker. Our resulting time-lapses show diverse changes in the world's most popular sites, like glaciers shrinking, skyscrapers being constructed, and waterfalls changing course.