Robust image copy detection method base on content

Abstract

The invention discloses a content-based robust image copy detection method, which has the following steps: to extract the feature vector of the testing image; to select the integral DCT transform coefficients of a plane Y of the original image YCbCr color space, to calculate the order measurement of the coefficients to obtain a coefficient sequence which is used as the feature vector of the testing image; then to establish testing image representative vector libraries; to execute clustering analysis to the feature vector sets of the testing image representative vector libraries to select the feature vectors closest to the clustering center as the clustering representatives to constitute a clustering representative vector library; to respectively search the matched testing image representative vector libraries to inquire the image feature vectors and the image feature vectors after rotation compensation, and to determine the belonged class; then to execute sequential matching search to each image feature vector in the clustering, and to determine whether the copy of the inquired image exists. The invention shows a higher robustness to help improve the inquiring efficiency, and has practical value as well as wide application in digital image database arrangement, digital image copyright protection and piracy tracking.

Description

technical field [0001] The invention belongs to multimedia processing technology, and relates to a content-based robust image copy detection method. Specifically, given a query image, the method can quickly and accurately judge and retrieve all copy images of the query image from a test image database . The present invention also relates to a method for extracting image feature vectors and an improved K-means clustering algorithm, the extracted image feature vectors can resist various conventional transformations and some low-intensity geometric transformations; the improved K-means clustering algorithm is introduced The class algorithm effectively reduces the clustering and matching retrieval time, and improves the query efficiency. Background technique [0002] With the development of network technology and multimedia technology, the duplication and dissemination of digital images has become very convenient. Therefore, the protection of intellectual property rights, espec...

AI Technical Summary

Problems solved by technology

Hsu (W.Hsu, T.S.Chua, and H.K.Pung, An integrated color-spatial approach to content-based image retrieval, in Proc.ACM Multimedia, 1995, pp.305-313.) etc. use images obtained by local color histograms The color information of the block is used as the image feature, and the two images are matched by comparing the local color histogram. The disadvantage is the high computational complexity and search time.

Chang (E.Y.Chang, J.Z.Wang, C.Li, G Wiederhold, RIME: replicated image detector for the world-wide-web, in: Proceedings of the SPIEMultimedia Storage and Archiving Systems, San Jose, CA, November 1998, Vol.III .) and other extraction using wavelet-based network image copy detection, the experimental results show that for a test image library with 30,000 images, it can correctly detect 8 copies, but it is powerless to deal with histogram quantization or high contrast enhancement transformation

Kim (Kim, C. Content-based image copy detection. Signal Processing: Image Communication, 2003, 18(3): 169-184) et al. proposed an image copy detection method based on block DCT coefficients, which flips and zooms images 180° , histogram quantization, blur, Gaussian noise, etc. all have good effects, but the rotation and cropping effects of the image are not good

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