Image retrieval with automatic query expansion based on local analysis in a semantical concept feature space
Md Mahmudur Rahman, Prabir Bhattacharya · 2009
We present an automatic query expansion approach by generalizing the vector space model of information retrieval. In this framework, the images are presented by vectors of weighted concepts similar to the keyword-based representation in the text retrieval domain. The concepts comprise of color and texture patches from local image regions in a multi-dimensional feature space. To generate the concept vocabularies and represent the images, statistical model is built by utilizing a multi-class Support Vector Machine (SVM)-based classification technique. For automatic query expansion, the correlations between concepts are analyzed based on the neighborhood proximity between the concepts in encoded images by considering the local feedback information. The experimental results on a photographic image collection demonstrate the effectiveness of the proposed query expansion approaches.