AI summary

This paper introduces TSI-PLSA, an extension of PLSA that incorporates spatial information, to learn object categories directly from noisy, unstructured image search engine results, achieving competitive performance on standard test sets compared to methods using hand-prepared datasets.

Abstract

Current approaches to object category recognition require datasets of training images to be manually prepared, with varying degrees of supervision. We present an approach that can learn an object category from just its name, by uti- lizing the raw output of image search engines available on the Internet. We develop a new model, TSI-PLSA, which extends PLSA (as applied to visual words) to include spa- tial information in a translation and scale invariant man- ner. Our approach can handle the high intra-class vari- ability and large proportion of unrelated images returned by search engines. We evaluate the models on standard test sets, showing performance competitive with existing meth- ods trained on hand prepared datasets.