Learning-based linguistic indexing of pictures with 2 - D MHMMs

Automatic linguistic indexing of pictures is an important but highly challenging problem for researchers in computer vision and content-based image retrieval. In this paper, we introduce a statistical modeling approach to this problem. Categorized images are used to train a dictionary of hundreds of concepts automatically based on statistical modeling. Images of any given concept category are regarded as instances of a stochastic process that characterizes the category. To measure the extent of association between an image and the textual description of a category of images, the likelihood of the occurrence of the image based on the stochastic process derived from the category is computed. A high likelihood indicates a strong association. In our experimental implementation, the ALIP (Automatic Linguistic Indexing of Pictures) system, we focus on a particular group of stochastic processes for describing images, that is, the two-dimensional multiresolution hidden Markov models (2-D MHMMs). We implemented and tested the system on a photographic image database of 600 different semantic cat- egories, each with about 40 training images. Tested using 3,000 images outside the training database, the system has demonstrated good accuracy and high potential in linguistic indexing of these test images.