The Distinctiveness, Detectability, and Robustness of Local Image Features

We introduce a new method that characterizes typical local image features (e.g., SIFT [9], phase feature [3]) in terms of their distinctiveness, detectability, and robustness to image deformations. This is useful for the task of classifying local image features in terms of those three properties. The importance of this classification process for a recognition system using local features is as follows: a) reduce the recognition time due to a smaller number of features present in the test image and in the database of model features; b) improve the recognition accuracy since only the most useful features for the recognition task are kept in the model database; and c) increase the scalability of the recognition system given the smaller number of features per model. A discriminant classifier is trained to select well behaved feature points. A regression network is then trained to provide quantitative models of the detection distributions for each selected feature point. It is important t...

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Gustavo Carneiro, Allan D. Jepson
• Keywords: Computer Vision, CVPR 2005, Feature Point, Image, Local Image Features