Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Media-based navigation for hypermedia systems
HYPERTEXT '93 Proceedings of the fifth ACM conference on Hypertext
Object-based navigation: an intuitive navigation style for content-oriented integration environment
HYPERTEXT '97 Proceedings of the eighth ACM conference on Hypertext
Towards a multimedia World-Wide Web information retrieval engine
Selected papers from the sixth international conference on World Wide Web
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Shape indexing by structural properties
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
WebSeer: An Image Search Engine for the World Wide Web
WebSeer: An Image Search Engine for the World Wide Web
Content-based image collection profiling and comparison via self-organised maps
Design and application of hybrid intelligent systems
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With the recent explosive growth in the volume of images on the World-Wide Web, it has become increasingly difficult to search for images of interests. The classification of images helps users to access a large image collection efficiently. Classification reduces search space by filtering out unrelated images. Classification also allows for more user-friendly interfaces: users can better visualize easily result space by browsing the representative images of the candidates. In this paper, we present a technique for image classification based on color, shape and composition using the primary objects. We apply this classification technique in image matching for image retrieval on the Web. Our experimental results show that this approach can maintain 73% of recall by searching only 24% of the whole data set. We also show how we apply such technique to assist users in navigation.