Query Decomposition: A Multiple Neighborhood Approach to Relevance Feedback Processing in Content-based Image Retrieval

Today's Content-Based Image Retrieval (CBIR) techniques are based on the "k-nearest neighbors" (kNN) model. They retrieve images from a single neighborhood using low-level visual features. In this model, semantically similar images are assumed to be clustered in the high-dimensional feature space. Unfortunately, no visual-based feature vector is sufficient to facilitate perfect semantic clustering; and semantically similar images with different appearances are always clustered into distinct neighborhoods in the feature space. Confinement of the search results to a single neighborhood is an inherent limitation of the k-NN techniques. In this paper we consider a new image retrieval paradigm ? the Query Decomposition model that facilitates retrieval of semantically similar images from multiple neighborhoods in the feature space. The retrieval results are the k most similar images from different relevant clusters. We introduce a prototype, and present experimental results t...

• Type: Conference
• Year: 2006
• Where: ICDE
• Authors: Kien A. Hua, Ning Yu, Danzhou Liu
• Keywords: Content-based Image Retrieval, Database, ICDE 2006, Image Retrieval Paradigm, Similar Images