Incremental parsing for latent semantic indexing of images

A generalized latent semantic analysis framework using a universal source coding algorithm for content-based image retrieval is proposed. By the multidimensional incremental parsing algorithm which is considered as a multidimensional extension of the Lempel-Ziv data compression method, a given image is compressed at a moderate bitrate while constructing the dictionary which implicitly embeds source statistics. Instead of concatenating all the corresponding dictionaries of an image corpus, we sequentially compress images using a previously constructed dictionary and end up with a visual lexicon which contains the least number of visual words covering all the images in the corpus. From the latent semantic analysis of the co-occurrence pattern of visual words over the images, a similarity between a given query and an image from the corpus is measured. An application of the proposed technique on a database of 20,000 natural scene images has demonstrated that the performance of the proposed system is favorable to that of existing approaches.