Probabilistic web image gathering
We propose a new method for automated large scale gathering of Web images relevant to specified concepts. Our main goal is to build a knowledge base associated with as many concepts as possible for large scale object recognition studies. A second goal is supporting the building of more accurate text-based indexes for Web images. In our method, good quality candidate sets of images for each keyword are gathered as a function of analysis of the surrounding HTML text. The gathered images are then segmented into regions, and a model for the probability distribution of regions for the concept is computed using an iterative algorithm based on the previous work on statistical image annotation. The learned model is then applied to identify which images are visually relevant to the concept implied by the keyword. Implicitly, which regions or the images are relevant is also determined. Our experiments reveal that the new method performs much better than Google Image Search and a simple method based on more standard content based image retrieval methods.