Feature selection for graph-based image classifiers
Date:
Talk at Iberic Patt. Rec. and Image Aanlysis conference in Estoril, Portugal.
Abstract: The interpretation of natural scenes, generally so obvious and effortless for humans, still remains a challenge in computer vision. We propose in this article to design binary classifiers able to recognise some generic image categories. Images are represented by graphs of regions and we define a graph edit distance to measure the dissimilarity between them. Furthermore a feature selection step is used to pick in the image the most meaningful regions for a given category and thus have a compact and appropriate graph representation.
[ Iberic Patt. Rec. and Image Aanlysis conference website / paper ]