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Research on Image Retrieval Optimization Based on SIFTLBP Combination
Zhao Qiang, Tang Meng
For the problem of accuracy, realtime performance and illumination in image retrieval and feature extraction with SIFT, a new image features extracting algorithm based on the SIFT was proposed and the Local Binary Patterns (LBP) algorithm. It used the same keypoint detection method as SIFT. After getting the keypoints of the image features, the SIFTLBP descriptor was made up of statistics of gradient information in 16×16 region and rotation invariant LBP value in 9×9 regions around each keypoint, and then building SIFTLBP Feature Descriptor of image in each pixel region as the center. Finally, the extracted features of data were selected based on genetic algorithm, and removed redundant information of feature point to reduce the dimension of the feature vector. Experimental results showed that the proposed algorithm had a good matching result on Visual Image Feature Extraction, it was validated that the algorithm was strongly robust to changes in lighting conditions, and improved the accuracy and the speed of retrieval.
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