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.
linguistics indexing research computer vision content-based image retrieval imaging imaging paper statistical models training dictionaries concept stochastic process measurement association experimentation implementation systems group multiresolution hidden markov model test image database database semantic accuracy image classification region matching machine learning image segmentation image wavelets learning