Centre for Computational Intelligence, N4 #B1a-02, Nanyang Avenue,

1. Presented by:Centre for Computational Intelligence School of Computer Engineering AN EVALUATION OF LOCAL IMAGE FEATURES FOR OBJECT CLASS RECOGNITION The use of local image features (LIF) for object class recognition is becoming increasingly popular. To better understand the suitability and power of existing LIFs for object class recognition, a simple but useful method is proposed in evaluation of such features. We have compared the performance of eight frequently used LIFs by the proposed method on two popular databases. We have used F-measure criterion for this evaluation. It is found that the individual performance of SURF and SIFT features are better than that of the global features on ETH-80 database with considerably lower number of training objects. However, it may not be good enough for more challenging object class recognition problem (e.g. Caltech-101). The evaluation of LIFs suggests the requirement for further investigation of more complementary LIFs. • Approximation of Bayesian Classification • Training • Extract all features (d) of each class • One k-d tree for each class. • NN classification • dj, c compute NN of dj in C: NNC(dj) Method: An approximation of Bayesian optimal classifier (Boiman et al. 08) List of features: Invariant to affine geometric and photometric transformations Results: Wide baseline matching (previously seen objects with viewpoint change) Databases: ETH-80 and Caltech-101 Results: Recognition rate of previously unseen objects. (different number of training images were used) Centre for Computational Intelligence, N4 #B1a-02, Nanyang Avenue, Nanyang Technological University, Singapore 639798., http://www.c2i.ntu.edu.sg