Automatic linguistic indexing of pictures by a statistical modeling approach

TLDR

Automatic linguistic indexing of images is a key but difficult challenge in computer vision and image retrieval. The study proposes a statistical modeling approach for automatic linguistic indexing of images. The authors train a dictionary of concept‑specific statistical models—specifically 2D multiresolution hidden Markov models—by treating images as stochastic process instances, compute likelihoods to associate images with textual concepts, and evaluate the system on 600 concepts with about 40 training images each against 4,600 test images, outperforming random annotation. Experiments show the system achieves good accuracy and demonstrates strong potential for linguistic indexing of photographic images.

Abstract

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 statistical models each representing a concept. Images of any given concept are regarded as instances of a stochastic process that characterizes the concept. To measure the extent of association between an image and the textual description of a concept, the likelihood of the occurrence of the image based on the characterizing stochastic process is computed. A high likelihood indicates a strong association. In our experimental implementation, we focus on a particular group of stochastic processes, that is, the two-dimensional multiresolution hidden Markov models (2D MHMMs). We implemented and tested our ALIP (Automatic Linguistic Indexing of Pictures) system on a photographic image database of 600 different concepts, each with about 40 training images. The system is evaluated quantitatively using more than 4,600 images outside the training database and compared with a random annotation scheme. Experiments have demonstrated the good accuracy of the system and its high potential in linguistic indexing of photographic images.