Image hashing combining adaptive grid descriptor and image energy
Traditional image segmentation methods perform non overlapping segmentation on images.The image hashing algorithm can only extract features from the pixels inside the image block.However,the simple linear iterative clustering superpixel segmentation algorithm can extract additional shape features while aggregating similar pixels.Therefore,this paper proposes an image hashing algorithm that combines an adaptive grid descriptor and image energy based on simple linear iterative clustering.First,the input image is preprocessed through bilinear interpolation and Gaussian low-pass filtering.Then,simple linear iterative clustering is used to perform superpixel segmentation on the preprocessed image,and an adaptive grid descriptor is applied to extract shape features from the superpixels.Second,pixels within the superpixels have similar brightness characteristics,so the energy values of each superpixel region are calculated based on brightness as the energy feature of the image.Finally,the shape feature sequence and the energy feature sequence are connected.The final hash sequence is obtained by encrypting the connected sequence.Experiments show that the proposed algorithm achieves a good balance between robustness and discrimination.The average operation time and hash length of the algorithm are 0.128 s and 467 bits respectively,which leads to a fast operational speed and a compact hash sequence.In terms of classification performance,when the false positive rate is 0,the true positive rate reaches 0.999 9.In terms of copy detection,both recall and precision are above 95％.In addition,compared with some similar algorithms,the proposed algorithm also has advantages in classification performance and copy detection.
万方数据
维普
