دانلود رایگان مقاله درهم سازی قدرتمند تصویر براساس زاویه بردار رنگ و عملگر کنی

Abstract

Image hashing is a novel technology of multimedia processing, and finds many applications, such as image forensics, image retrieval and image indexing. Conventional image hashing algorithms have limitations in reaching desirable classification performances between rotation robustness and discrimination. Aiming at this issue, we propose a robust image hashing based on color vector angle and Canny operator. Specifically, our hashing firstly converts input image to a normalized image by interpolation and Gaussian low-pass filtering. And then, color vector angles and image edges are both extracted from the normalized image. Finally, statistical features incorporating color vector angles and image edges are calculated to form image hash. We conduct experiments with 2762 images to validate efficiency of our hashing. The experimental results show that our hashing is robust against normal digital processing, such as image rotation, brightness/contrast adjustment and JPEG compression, and reaches good discrimination. Receiver operating characteristics (ROC) curve comparisons with some state-of-the-art algorithms indicate that our hashing outperforms these compared algorithms in classification performances between robustness and discriminative capability.

5. Conclusions

In this work, we have proposed a robust image hashing based on color vector angle and Canny operator. Our image features are extracted from color vector angles of edge pixels on the circles. Since edge pixels on the circles are kept unchanged after rotation, our extracted features are invariant to image rotation and therefore make our hashing good rotation robustness. As color vector angle calculation fully exploits all RGB color components, our hashing reaches desirable discriminative capability. Experimental results have shown that our hashing is robust against normal content-preserving manipulations, such as image rotation with arbitrary angle, brightness adjustment, contrast adjustment, JPEG compression, gamma correction, 3 × 3 Gaussian low-pass filtering, watermark embedding and image scaling. ROC curve comparisons with some state-of-the-art algorithms have indicated that our hashing outperforms the compared algorithms inclassificationperformances between robustness and discriminative capability.