AN EVALUATION OF LOCAL IMAGE FEATURES FOR OBJECT CLASS RECOGNITION 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. Acronym Name Dimension SIFT Scale Invariant Feature Trans 128 SURF Speed-Up Robust Features 64 GLOH Grad Location Orientation Hist GM Gradient Moment 20 SF Steerable Filters 14 CMI Color Moment Invariant 18 RMI Revised Moment Invariant 15 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) List of features: Invariant to affine geometric and photometric transformations Method: An approximation of Bayesian optimal classifier (Boiman et al. 08) SIFT SURF GLOH GM SF CMI RMI Apple 0.95 0.99 0.89 0.84 0.10 0.70 0.04 Car 1.00 0.96 0.62 0.15 Cow 0.98 0.94 0.83 0.46 0.24 Cup 0.67 0.05 Dog 0.90 0.61 0.79 0.21 Horse 0.97 0.91 0.86 0.66 0.17 Pear 0.41 0.09 Tomato 0.23 Avg 0.984 0.43 0.12 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