Keypoints derivation for object class detection with SIFT algorithm

Authors:
Krzysztof Slot;Hyongsuk Kim
Affiliations:
Lodz and Academy of Humanities and Economics, Institute of Electronics, Technical University of Lodz, Lodz, Poland;Division of Electronics and Information Engineering, Chonbuk National University, Chonju, Republic of Korea
Venue:
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Year:
2006

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Abstract

The following paper proposes a procedure for SIFT keypoints derivation for the purpose of object class detection. The main idea of the method is to build appropriate object class keypoints by extracting information that corresponds to characteristic class features. The proposed procedure is composed of two main steps: clustering of similar SIFT keypoints and derivation of appropriate keypoint descriptors. Face detection in images has been selected as a sample application for the proposed approach performance evaluation.