基于哈希的圖像搜索技術(shù)

1、基于哈希的圖像搜索技術(shù)基于哈希的圖像搜索技術(shù)提綱背景典型方法評(píng)價(jià)指標(biāo)總結(jié)提綱背景典型方法評(píng)價(jià)指標(biāo)總結(jié)背景背景圖片搜索引擎移動(dòng)搜索業(yè)務(wù)皮革紡織醫(yī)學(xué)背景圖片搜索引擎移動(dòng)搜索業(yè)務(wù)皮革紡織醫(yī)學(xué)背景為什么要用哈希？背景為什么要用哈希？背景怎樣對(duì)圖像特征進(jìn)行哈希編碼？背景怎樣對(duì)圖像特征進(jìn)行哈希編碼？背景背景背景110110010110001000000001背景110110010110001000000001背景110110010110001000000001Hamming distance:14背景110110010110001000000001Hamm背景背景100001110111000101Dat

2、asetSimple hash table背景100001110111000101DatasetSimpl100001110111000101DatasetQ110111Simple hash tableXOR背景100001110111000101DatasetQ1101100001110111000101 NQDatasetSearch the hash table for a small set of images.Q110111Simple hash tableXOR背景100001110111000101 NQDataset背景背景特征進(jìn)行哈希編碼的本質(zhì)？背景特征進(jìn)行哈希編碼的本質(zhì)？

3、背景Gionis, A. & Indyk, P. & Motwani, R. (1999)Take random projections of dataQuantize each projection with few bits010101101No learning involvedGist descriptor典型方法 局部敏感哈希 Local Sensitive HashingGionis, A. & Indyk, P. & MotwaThe probability that a random hyperplane separates two unit vectors depends o

4、n the angle between them:Goemans and Williamson 1995, Charikar 2004, modified by meHigh dot product: unlikely to splitLower dot product: likely to splitCorresponding hash function:典型方法 局部敏感哈希 Local Sensitive HashingThe probability that a random Ground Truth32 bit128 bitA LSH family, H(c, r, P1, P2),

5、 for any p, q belong to S, If |p-q| r, then Pr(h(p)=h(q) P1 If |p-q| cr, then Pr(h(p)=h(q) P2256 bit典型方法 局部敏感哈希 Local Sensitive HashingGround Truth32 bit128 bitA LSH典型方法 局部敏感哈希 Local Sensitive Hashing典型方法典型方法 迭代量化編碼 Iterative Quantization典型方法Average quantization error: 1.00(a) PCA aligned.Average qu

6、antization error: 0.93(b) Random Rotation.Average quantization error: 0.88(c) Optimized Rotation.Q(B,R)=|B-VR|F21000011111100001010010111. S.Lazebnik, A. Gordo, and F. Perronnin, “Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large- scale Image Retrieval,” Accepted, IEE

7、E Trans. Pattern Analysis and Machine Learning Intelligence , 2012.典型方法 迭代量化編碼 Iterative QuantizationAverage quantization error: 1.1. S.Lazebnik, A. Gordo, and F. Perronnin, “Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large- scale Image Retrieval,” Accepted, IEEE Tra

8、ns. Pattern Analysis and Machine Learning Intelligence , 2012.典型方法 迭代量化編碼 Iterative Quantization1. S.Lazebnik, A. Gordo, aRRITQ32 bitLSHPCAH典型方法 迭代量化編碼 Iterative QuantizationRRITQ32 bitLSHPCAH典型方法典型方法迭稀疏迭代量化編碼 Sparse Projections for High-Dimensional Binary Codes1. Y. Xia, K. He, P. Kohli, and J. Sun

9、, “Sparse Projections for High-Dimensional Binary Codes,” in CVPR, 2015.典型方法1. Y. Xia, K. He, P. Koh典型方法 核監(jiān)督哈希 Supervised Hashing with Kernels1. W. Liu, W. Jun, R. Ji, Y. Jiang, and S. Chang, “Supervised hashing with kernels,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 2074-2081, 2013.典型方法1. W. Li