Condensing Image Databases when Retrieval is Based on Non-Metric Distances

One of the key problems in appearance-based vision is understanding how to use a set of labeled images to classify new images. Classification systems that can model human performance, or that use robust image matching methods, often make use of similarity judgments that are non-metric; but when the triangle inequality is not obeyed, most existing pattern recognition techniques are not applicable. We note that exemplar-based (or nearest-neighbor) methods can be applied naturally when using a wide class of non-metric similarity functions. The key issue, however, is to find methods for choosing good representatives of a class that accurately characterize it. We show that existing condensing techniques for finding class representatives are ill-suited to deal with non-metric dataspaces. We then focus on developing techniques for solving this problem, emphasizing two points: First, we show that the distance between two images is not a good measure of how well one image can represent another...

• Type: Conference
• Year: 1998
• Where: ICCV
• Authors: David W. Jacobs, Daphna Weinshall, Yoram Gdalyahu
• Keywords: Computer Vision, ICCV 1998, Image, Image Matching Methods, Non-metric Spaces