Feature weighting in visual product recognition

Significant progress towards visual search has been made in the past two decades through the development of local invariant features. Among existing local feature detectors, the Scale Invariant Feature Transform (SIFT) is widely used since it is designed to be invariant to minimal illumination changes and certain geometric transformations. However, in practice, the recognition performance is still subject to actual condition. Some keypoints are more stable while others are less stable and can not be repeatedly detected. Besides, in visual object recognition where the foreground object is to be recognized while the background suppressed, the current scalable vocabulary tree (SVT) framework treats each descriptor as equally important, hence restricting its performance. This paper aims to study the effect of SIFT respect to illumination and geometric changes and develop a feature weighting algorithm to incorporate the stability of SIFT and saliency information into weighted scalable vocabulary tree (WSVT) based recognition. Experimental results on a commercial product database show the proposed feature weighting algorithm outperforms the baseline SVT recognition by 5%.