Enhancement of textual images classification using segmented visual contents for image search engine
This paper deals with the use of the dependencies between the textual indexation of an image (a set of keywords) and its visual indexation (colour and shape features). Experiments are realized on a corpus of photographs of a press agency (EDITING) and on another corpus of animals and landscape photographs (COREL). Both are manually indexed by keywords. Keywords of the news photos are extracted from a hierarchically structured thesaurus. Keywords of Corel corpus are semantically linked using WordNet database. A semantic clustering of the photos is constructed from their textual indexation. We use two different visual segmentation schemes. One is based on areas of interest, the other one on blobs of homogenous colour. Both segmentation schemes are used to evaluate the performance of a content-based image retrieval system combining textual and visual descriptions. Results of visuo-textual classifications show an improvement of 50% against classification using only textual information. Finally, we show how to apply this system in order to enhance a web image search engine. To this purpose, we illustrate a method allowing selecting only accurate images resulting from a textual query.
segmentation content-based image retrieval (cbir) visuo-textual fusion vectorial model multimedia indexing kullback-leibler distance