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Combination Way of Local Properties, Classifiers and Saliency in Bag-of- Keypoints Approach for Generic Object Recognition


Shogo Nakamoto, Takashi Toriu


Vol. 11  No. 5  pp. 35-42


Csurka et. al. proposed a bag-of-keypoints approach which represents an image by a histogram of the number of occurrences of local properties at keypoints. In this approach, Scale Invariant Feature Transform (SIFT) descriptors are utilized for generic object recognition. As an alternative to SIFT, a method based on Speeded Up Robust Features (SURF) are reported to give better performance at greater speeds than SIFT. In this paper, we investigate combination way of SIFT or SURF and current various classifies such as Na?ve Bayes, SVM and so on. We also propose a scheme in which a saliency map is utilized for removing irrelevant keypoints. We demonstrate that removing keypoints based on saliency improves classification rate in some situation.


Generic object recognition, SIFT, SURF, Saliency map, bag-of-keypoints