Image Mining Techniques Research Articles

Using text and image mining to study how actor engagement creates value in the sharing economy

PurposeThis paper aims to explore the concept of “actor engagement” within the context of the sharing economy, a novel and dynamic business model. Specifically, it investigates the formation of actor engagement and its relationship with value creation within this business model.Design/methodology/approachDrawing on Storbacka et al. (2016)actor engagement framework and service-dominant logic service ecosystem model, unstructured data (text and images) from the Airbnb platform in seven countries and text- and image-mining techniques such as machine learning are used to measure the research variables and test the model by PLS-SEM.FindingsThe results indicate that affective engagement has a more significant impact on behavioural engagement than cognitive engagement for multidimensional actor engagement. Service providers’ engagement – directly, and through customer engagement – influences value creation for service providers (i.e. performance). Moreover, national-level moderator (i.e. economic, competitiveness, technology, social and political factors) plays a significant moderating role in our model.Research limitations/implicationsThis study encourages future research to explore how actor engagement leads to value creation for all actors on the different sharing economy platforms.Practical implicationsThe findings provide practical insights for service providers to engage their customers and platform managers, especially in an international context, on managing their relationships with both customers and service providers in different countries.Originality/valueThis study advances the current literature on actor engagement and its role in value creation by providing a better understanding of the role of the national context in this process through unstructured data analysis.

Enhancing Diagnostic Decision-Making with Image Mining Techniques: A Proposed Framework for Medical Images

Enhancing Diagnostic Decision-Making with Image Mining Techniques: A Proposed Framework for Medical Images

To see and then to believe: how image affect tenant decision-making and satisfaction on short-term rental platform

Home sharing is a new industry that was born with the development of sharing economy. The online short-term rental platform is an important carrier for home sharing. On the short-term rental platform, house images are an essential way to display the overall situation of the house, and one of the main channels for tenants to obtain house information. This paper studies the relationship between house images' colors and contents and tenant booking decision-making and satisfaction. By utilizing image mining techniques on the data obtained from a popular short-term rental platform in China, this research reveals that the color richness of house images has a significant negative relationship with both tenant booking decision-making and tenant satisfaction. What's more, both household and leisure content displayed in house images has significant positive relationships with tenant booking decision-making. Our work supplements the research on the impact of house images on home sharing and provides meaningful guidance for both the short-term rental platforms and the landlords.

Effective diagnosis mechanism for skin disorders using image mining techniques

Owing to its costly diagnosis, which requires elevated human interpretation, skin conditions are one of the major medical problems. In assessing the chance of getting treated, early-stage diagnosis of skin disorders plays a crucial role. We assume that the use of an accurate classifier helps to diagnose early-stage skin disorders. Therefore, we present a fully automated classification system in this article for diagnosing skin disorders by using image mining techniques. Our model is designed to compromise pixel, object and pattern levels in three steps, respectively. We have used multiple image mining techniques like augmentation, feature extraction, classification in order to classify skin disorder images effectively.

Image mining technique using Hadoop map reduce over distributed multi-node computers connections

The amount of today's image collections has exploded to petabytes of data. A fair amount of time cannot be allotted for the analysis of such massive datasets using a personal computer. As a result, distributed computing is required for current image collection mining. This work used multi-nodes-computers for image mining in order to improve reliability, fall tolerance, and time efficiency. Because of this, the data was divided up across the nodes in the Hadoop multi-node cluster, and the results were then compiled to create an image clustering algorithm. One master node and two slave nodes were used to test our technique on a huge dataset. Using multi-node Hadoop, we found that we could get a speed-up in implementing as high as a single-node Hadoop implementation.

Image mining technique using Hadoop map reduce over distributed multi-node computers connections

The amount of today's image collections has exploded to petabytes of data. A fair amount of time cannot be allotted for the analysis of such massive datasets using a personal computer. As a result, distributed computing is required for current image collection mining. This work used multi-nodes-computers for image mining in order to improve reliability, fall tolerance, and time efficiency. Because of this, the data was divided up across the nodes in the Hadoop multi-node cluster, and the results were then compiled to create an image clustering algorithm. One master node and two slave nodes were used to test our technique on a huge dataset. Using multi-node Hadoop, we found that we could get a speed-up in implementing as high as a single-node Hadoop implementation.

An exploration of the relation between the visual attributes of thumbnails and the view-through of videos: The case of branded video content

While browsing through an online video platform, potential viewers decide which videos to click and watch based on the information and impression they obtain from thumbnails. Therefore, a thumbnail needs to be able to tell potential viewers what the video is about (i.e., be informative), and at the same time, a thumbnail needs to grab potential viewers' attention (i.e., be visually appealing). Drawing from the elaboration likelihood model (ELM), we take the visual attributes that are relevant to the informativeness of thumbnails (e.g., element complexity and object complexity) as central route cues, take the visual attributes that are relevant to the aesthetics of thumbnails (e.g., celebrity endorsement, colorfulness, brightness, and image quality) as peripheral route cues, and explore the relation between these visual attributes of thumbnails and the view-through of videos using 3,745 marketing videos posted by 38 top brands across 4 industries – the automobile, beverage, restaurant, and toy industries – on YouTube. We construct our variables by extracting features from thumbnails using image mining techniques. Our study contributes to the literature on information systems and marketing by enhancing our understanding of the role of the visual attributes of images in persuading viewers in the online environment. Furthermore, our study provides a possible theoretical basis for studies on information processing and decision support that focus on automatic thumbnail selection. Finally, our study provides useful and practical guidelines for designing templates for optimal thumbnails that grab potential viewers' attention and yield more video views.

A Survey Paper on Image Mining Techniques and Classification Brain Tumor

Image mining is a method of searching and discovering the valuable information and knowledge from a set of huge image data. Image mining essentially depends on the data mining, digital image processing, machine learning, image retrieval, and artificial intelligence. Image mining is a process which is conducted to extract the hidden information such as image data and the additional pattern that could not be observed from the image. The main problems could face the mining of the collected images can be summarized in two main points: first is the image must be suitable for the mining process and second is the image’s chosen objects and features in order to be treated to extract the most effective route to save the time, and to save the effort. This paper is a survey presents the steps of the image mining process and represented an intensive view on using the image mining to the classify the brain tumors. In addition, it’s proposed a general scheme to accomplish the processes and to analysis the latest techniques which have been used to classify the brain tumors with comparison to the training groups and the amount of accuracy that obtained from the analysis. In addition, the paper compares the relevant and most recent published literature. The high published accuracy claim to be 98% which was obtained using the Deep convolutional neural network (DCNN).

Aplicando Mineração de Imagens na Agricultura de Precisão

Crop image analysis are consolidated in the precision farming market. In this sense, the use of image processing techniques, image mining and artificial intelligence are fundamental tools. Being able to apply these techniques individually or together. A common problem in image analysis is that small changes in lighting and timing can influence how computational techniques identify its elements. The cost is too high or even unfeasible to universally identify or segment an image. As such, a solid starting point is needed to guide existing techniques. This study presents an experiment using image mining techniques, associated with custom association algorithms. Using expert knowledge to create and label pixel set of interest. Thus, when processing an image, the classes of interest are easily identified and adjusted for each reality. The empirical results indicate that our solution improves the way of selecting patterns by identifying the classes of interest, correctly identifying soil and vegetation. Tests were performed on seven different mosaics from the same culture. The process of identifying the desired classes (soil, plantation) occurred satisfactorily, thus validating our study as a viable solution for precision agriculture.

Green-gradient based canopy segmentation: A multipurpose image mining model with potential use in crop phenotyping and canopy studies

Efficient quantification of the sophisticated shading patterns inside the 3D vegetation canopies may improve our understanding of canopy functions and status, which is possible now more than ever, thanks to the high-throughput phenotyping (HTP) platforms. In order to evaluate the option of quantitative characterization of shading patterns, a simple image mining technique named “Green-gradient based canopy Segmentation Model (GSM)” was developed based on the relative variations in the level of RGB triplets under different illuminations. For this purpose, an archive of ground-based nadir images of heterogeneous wheat canopies (cultivar mixtures) was analyzed. The images were taken from experimental plots of a two-year field experiment conducted during 2014–15 and 2015–16 growing seasons in the semi-arid region of southern Iran. In GSM, the vegetation pixels were categorized into the maximum possible number of 255 groups based on their green levels. Subsequently, the mean red and the mean blue levels of each group were calculated and plotted against the green levels. It is evidenced that the yielded graph could be readily used for (i) identifying and characterizing canopies even as simple as one or two equation(s); (ii) classification of canopy pixels in accordance with the degree of exposure to sunlight; and (iii) accurate prediction of various quantitative properties of canopy including canopy coverage (CC), Normalized difference vegetation index (NDVI), canopy temperature, and also precise classification of experimental plots based on the qualitative characteristics such as subjection to water and cold stresses, date of imaging, and time of irrigation. The introduced model may provide a multipurpose HTP platform and open new windows to canopy studies.

RETRACTED ARTICLE: Quality of service (Qos) and priority aware models for adaptive efficient image retrieval in WSN using TBL routing with RLBP features

The problem of image retrieval in wireless sensor networks has been well studied. Towards image retrieval in WSN, various methods have been discussed earlier, but suffer to achieve higher performance in image mining. To improve the performance, an efficient QoS adaptive image mining technique has been presented in this paper. The method focused on the efficiency in image mining as well as achieving QoS in WSN. The image has been processed to remove the noise by applying Gabor filters. From the noise removed image, the local binary pattern has been generated at each region of the image to produce regional local binary pattern (RLBP). The RLBP feature extracted has been used to measure the similarity between various images. The method maintains in the taxonomy of various image classes, and each class has different features. The input query has been measured for its similarity towards various classes from taxonomy. According to the similarity a single class has been identified. Based on the class identified, a subset of nodes from WSN has been identified where the relevant Images are available. To reach the data nodes the method identifies the list of routes and estimates traffic bandwidth latency (TBL) support. Based on the value of TBL support a specific route has been selected to perform image retrieval. The RLBP feature generated has been transferred to the data nodes, where the method estimates RLBPS (RLBP similarity). According to the value of RLBP similarity, subsets of images have been selected and transmitted the source node. The method improves the performance of image mining in WSN with less complexity.

Improved Deep Convolution Neural Networks Classification Amygdala of Image Mining Technique using ASD Accuracy

Image mining is as of now a developing dynamic research center in computer science. Generally clear inside the initial couple of long periods of life. One of as of late most seriously contemplated related with fMRI a differing gathering of medicinal conditions. In mentally unbalanced kids this actuation is anomalous of their moms mining lessened when Images of outsiders indicated. Functional magnetic resonance imaging to distinguish Tuberous Sclerosis bigger right amygdala volume has been related with more extreme social and open hindrances in mentally unbalanced two year olds. This work, semantic division as relating exhibited bigger amygdalae structures among kids with profound convolutions neural systems. Conduct articulations of the extreme introvert tend coming about basic and utilitarian irregularities in the cerebrum. Promising Tools Neuro Imaging Analysis Kit (NIAK) for UCI Dataset Autism Screening Adult(ASA) division sushisen algorithm utilizing essentially includes decrease systems less amygdala enactment amid the assignment enhance fMRI check and the Issue arrangement CNN rise 94.88 percent accuracies of a rate.

Understanding tourists\u2019 urban images with geotagged photos using convolutional neural networks

This study aims to track down representative images and elements of sightseeing attractions by analyzing the photos uploaded on Flickr by Seoul tourists with the image mining technique. For this purpose, we crawled the photos uploaded on Flickr and classified users into residents and tourists; drew 11 region of attractions (RoA) in Seoul by analyzing the spatial density of the photos; classified the photos into 1000 categories and then 14 categories by grouping 1000 categories by utilizing Inception V3 model; analyzed the characteristics of the photo image by RoA. Key findings of this study are that tourists are interested in old palaces, historical monuments, stores, food, etc. and those key elements are distinguished from the major sightseeing attractions in Seoul. More specifically, tourists are more interested in palaces and cultural assets in Jongno and Namsan, food and restaurants in Shinchon, Hongdae, Itaewon, Yeouido, Garosu-gil, and Apgujeong, war monuments or specific artifacts in War Memorial and the National Museum of Korea, facilities, temples, and pictures of cultural properties in Samsung Station, and toyshops in Jamsil. This study is meaningful in three folds: first, it tries to analyze urban image through the photos posted on SNS by tourists. Second, it uses deep learning technique to analyze the photos. Third, it classifies and analyzes the whole photos posted by Seoul tourists while most of other researches focus on only specific objects. However, this study has a limitation because the Inception v3 model which has been used in this research is a pre-trained model created by training the ImageNet data. In future research, it is necessary to classify photo categories according to the purpose of tourism and retrain the model by creating new training data set focusing on elements of Korea.

Fashion recommendations through cross-media information retrieval

Fashion recommendation has attracted much attention given its ready applications to e-commerce. Traditional methods usually recommend clothing products to users on the basis of their textual descriptions. Product images, although covering a large resource of information, are often ignored in the recommendation processes. In this study, we propose a novel fashion product recommendation method based on both text and image mining techniques. Our model facilitates two kinds of fashion recommendation, namely, similar product and mix-and-match, by leveraging text-based product attributes and image features. To suggest similar products, we construct a new similarity measure to compare the image colour and texture descriptors. For mix-and-match recommendation, we firstly adopt convolutional neural network (CNN) to classify fine-grained clothing categories and fine-grained clothing attributes from product images. Algorithm is developed to make mix-and-match recommendations by integrating the image extracted categories and attributes information are with text-based product attributes. Our comprehensive experimental work on a real-life online dataset has demonstrated the effectiveness of the proposed method.

Identification of Uterine Fibroids using Enhanced Image Mining Techniques: Bio-Inspired Xenogenetic Based Extreme Learning Neural Network Classification with Improved Fireflies Hausdorff Distance

Identification of Uterine Fibroids using Enhanced Image Mining Techniques: Bio-Inspired Xenogenetic Based Extreme Learning Neural Network Classification with Improved Fireflies Hausdorff Distance

Image Mining Process and Techniques Using Association Rule – A Survey

Image Mining Process and Techniques Using Association Rule – A Survey

A multi-language writer identification method based on image mining and genetic algorithm techniques

Writing identification based on handwriting has many applications in the real world. Due to various forms of written letters in different languages, one of the major challenges in this context is offering an efficient method not being dependent on any specific language. In this paper, we have proposed a new approach based on image mining techniques for offline and text independent writer identification. In this method, each writers’ prominent features are found from training samples, and then identification is done according to them. In the image mining part of the proposed approach, certain techniques including SVM classifier and genetic algorithms are employed. To evaluate this method and show its performance in different languages, CASIA for Chinese, IAM dataset for English and two datasets for Kannada and Persian language handwriting were examined. The experiment results demonstrate that the presented method has over 99% accuracy for these languages. Regarding the results in tested languages and the method details, it is highly likely that our method would have good results in other languages also.

A Big Data Based Edge Detection Method for Image Pattern Recognition - A Survey

In Today’s era Big Data is one of the most well-known research area that try to solve many research problems. The focus is mainly on how to come out those problems of Big Data and it could be handling in recent systems. Image mining and genetic algorithm is used to automate the process of images, patterns, data sets and etc. Image mining is used to extract the hidden images from the set of images. Genetic algorithm is also quite effective in solving certain optimization and intelligence problems and it is used in many applications, including image pattern recognition. The survey paper reviews of Big Data with edge detection methods on various types of images. In edge detection image pattern recognition is to choose the best images from the group of images by using both image mining and genetic algorithm techniques

Nitrogen and potassium deficiency identification in maize by image mining, spectral and true colour response

Nutrients namely nitrogen (N) and potassium (K) management is an important agronomic practice to attain higher yield. The present study was conducted to determine effective spectra ranges and significant component images of RGB intensities which restrict ineffective data to be processed in identifying N and K deficiency symptoms through image mining, hyperspectral and true color responses. Maize crop was grown under field condition as per the recommended package of practice. At seed development stage, the leaf reflectance and digital images were acquired from N and K deficient leaves along with control (N and K sufficient) leaves. A portable spectroradiometer capable of measuring the wavelength range of 350–1050 nm of the electromagnetic spectrum was used to collect spectral data. The digital image was acquired using 20.1 megapixel camera. The result indicated that N and K deficiency increased the leaf reflectance at two ranges of green (centered 555 nm) and red edge (centered 715 nm). The K deficient leaf showed increased reflectance at near infrared (NIR) region of the spectrum. Differences in spectral reflectance of the leaves were highly correlated to Red, Green and Blue intensity values of N and K deficient leaves. High values for the blue and red portions suggests that chlorophyll and other associated pigments are not as plentiful in the N and K deficient plants, and higher reflectance values in the green correlates with more yellow pigment and decreased plant functions. The results indicated that identification of nutritional deficiency symptoms through image mining techniques could yield improved accuracy using spectra range and significant component images of RGB intensities that resonate with the physiological changes in crop due to the deficiency.

Green digitization: Online botanical collections data answering real‐world questions

Green digitization: Online botanical collections data answering real‐world questions