Applying Visual Attention Computational Model and Latent Semantic Indexing to image retrieval
Latent Semantic Indexing(LSI), as a popular textual information retrieval approach, has been used heavily for many years. However, the use of the approach in image retrieval has been limited. In this paper, a method of using LSI in combination with the salient image representation based on a saliency-based bottom-up Visual Attention Computational Model(VACM) motivated by visual physiological experimental results was proposed. Firstly, VACM was used to detect the salient region of the image, which is composed of salient or interest points. Meanwhile, a method to select number of the salient points to be extracted for each image is presented. Then color and texture features of the pixels in a small window around each salient point was computed as the Visual Attention Descriptor(VAD). VADs of all images in the training data-set were to be clustered by K-Means algorithm to construct a codebook. The term-document matrix was to be built based on the codebook and VADs of all images in the test data-set. Then LSI with four weighting schemes were applied to the termdocument matrix for image retrieval. According to the preliminary experiment results, some conclusions can be drawn. The best weighting for the LSI technique appears to be applying both local and global weight. With experimental image database used in this paper, the optimal number of dimensions of the semantic space for the LSI model, i.e., the rank of the approximation matrix, was found to be about 21 and the optimal codebook's size should be 900.