SVD-SIFT for web near-duplicate image detection

Stable and high distinctive image features are the basis for web near-duplicate image detection. SIFT (scale invariant feature transform) not only has good scale and brightness invariance, also has a certain robustness to affine distortion, perspective change, and additive noise. However, to extract SIFT features to represent an image, hundreds or even thousands of SIFT key points need to be selected. And each key point needs to be described by using a 128-dimensional feature vector. Thus, the matching cost of detection method based on SIFT features is high. In this paper, we propose to apply the singular value decomposition (SVD) method for feature matching and extract the new features from the set of SIFT feature points. The extracted feature is termed as SVD-SIFT. Experimental results demonstrate that the method can obtain a better tradeoff between effectiveness and efficiency for detection.

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