Algorithms for clustering data
Algorithms for clustering data
International Journal of Computer Vision
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Self-organizing maps
Image retrieval using hierarchical self-organizing feature maps
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
A Relevance Feedback Architecture for Content-based Multimedia Information Retrieval Systems
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
Interactive Learning with a "Society of Models"
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
NeTra: a toolbox for navigating large image databases
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Supporting Content-based Queries over Images in MARS
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
Color Clustering Techniques for Color-Content-Based Image Retrieval from Image Databases
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
Visual structures for image browsing
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Information Processing and Management: an International Journal - Special issue: Cross-language information retrieval
Journal of the American Society for Information Science and Technology
Exploring the relationship between feature and perceptual visual spaces
Journal of the American Society for Information Science and Technology
A Multi-class Kernel Alignment Method for Image Collection Summarization
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
An interactive approach for filtering out junk images from keyword-based google search results
IEEE Transactions on Circuits and Systems for Video Technology
System architecture of a web service for Content-Based Image Retrieval
Proceedings of the ACM International Conference on Image and Video Retrieval
Concept based interactive retrieval for social environment
Proceedings of the 2010 ACM workshop on Social, adaptive and personalized multimedia interaction and access
A kernel-based framework for image collection exploration
Journal of Visual Languages and Computing
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The goal of this paper is to describe an exploration system for large image databases in order to help the user understand the database as a whole, discover hidden relationships, and formulate insights with minimum effort. While the proposed system works with any type of low-level feature representation of images, we describe our system using color information. The system is built in three stages: the feature extraction stage in which images are represented in a way that allows efficient storage and retrieval results closer to the human perception; the second stage consists of building a hierarchy of clusters in which the cluster prototype, as the electronic identification ( e ID) of that cluster, is designed to summarize the cluster in a manner that is suited for quick human comprehension of its components. In a formal definition, an electronic IDentification ( e ID) is the most similar image to the other images from a corresponding cluster; that is, the image in the cluster that maximizes the sum of the squares of the similarity values to the other images of that cluster. Besides summarizing the image database to a certain level of detail, an e ID image will be a way to provide access either to another set of e ID images on a lower level of the hierarchy or to a group of perceptually similar images with itself. As a third stage, the multi-dimensional scaling technique is used to provide us with a tool for the visualization of the database at different levels of details. Moreover, it gives the capability for semi-automatic annotation in the sense that the image database is organized in such a way that perceptual similar images are grouped together to form perceptual contexts. As a result, instead of trying to give all possible meanings to an image, the user will interpret and annotate an image in the context in which that image appears, thus dramatically reducing the time taken to annotate large collection of images.