MUSE: A Content-Based Image Search and Retrieval System Using Relevance Feedback
Multimedia Tools and Applications
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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 unrelated images. In this paper, we present a technique for image classification based on color, shape and composition using the primary objects. Our experimental results show that this approach can maintain 73% of recall just by searching 24% of the whole data set. The classification results are also used to improve the users' interaction. The retrieval candidates are categorized based on the primary objects and presented to the users. We also show how we apply such technique to assist users in navigation.