Feature hashing

About: Feature hashing is a research topic. Over the lifetime, 993 publications have been published within this topic receiving 51462 citations.

Distributed Adaptive Binary Quantization for Fast Nearest Neighbor Search

Abstract: Hashing has been proved an attractive technique for fast nearest neighbor search over big data. Compared with the projection based hashing methods, prototype-based ones own stronger power to generate discriminative binary codes for the data with complex intrinsic structure. However, existing prototype-based methods, such as spherical hashing and K-means hashing, still suffer from the ineffective coding that utilizes the complete binary codes in a hypercube. To address this problem, we propose an adaptive binary quantization (ABQ) method that learns a discriminative hash function with prototypes associated with small unique binary codes. Our alternating optimization adaptively discovers the prototype set and the code set of a varying size in an efficient way, which together robustly approximate the data relations. Our method can be naturally generalized to the product space for long hash codes, and enjoys the fast training linear to the number of the training data. We further devise a distributed framework for the large-scale learning, which can significantly speed up the training of ABQ in the distributed environment that has been widely deployed in many areas nowadays. The extensive experiments on four large-scale (up to 80 million) data sets demonstrate that our method significantly outperforms state-of-the-art hashing methods, with up to 58.84% performance gains relatively.

Multimodal Discriminative Binary Embedding for Large-Scale Cross-Modal Retrieval

Abstract: Multimodal hashing, which conducts effective and efficient nearest neighbor search across heterogeneous data on large-scale multimedia databases, has been attracting increasing interest, given the explosive growth of multimedia content on the Internet. Recent multimodal hashing research mainly aims at learning the compact binary codes to preserve semantic information given by labels. The overwhelming majority of these methods are similarity preserving approaches which approximate pairwise similarity matrix with Hamming distances between the to-be-learnt binary hash codes. However, these methods ignore the discriminative property in hash learning process, which results in hash codes from different classes undistinguished, and therefore reduces the accuracy and robustness for the nearest neighbor search. To this end, we present a novel multimodal hashing method, named multimodal discriminative binary embedding (MDBE), which focuses on learning discriminative hash codes. First, the proposed method formulates the hash function learning in terms of classification, where the binary codes generated by the learned hash functions are expected to be discriminative. And then, it exploits the label information to discover the shared structures inside heterogeneous data. Finally, the learned structures are preserved for hash codes to produce similar binary codes in the same class. Hence, the proposed MDBE can preserve both discriminability and similarity for hash codes, and will enhance retrieval accuracy. Thorough experiments on benchmark data sets demonstrate that the proposed method achieves excellent accuracy and competitive computational efficiency compared with the state-of-the-art methods for large-scale cross-modal retrieval task.

Large-Scale Video Hashing via Structure Learning

Abstract: Recently, learning based hashing methods have become popular for indexing large-scale media data. Hashing methods map high-dimensional features to compact binary codes that are efficient to match and robust in preserving original similarity. However, most of the existing hashing methods treat videos as a simple aggregation of independent frames and index each video through combining the indexes of frames. The structure information of videos, e.g., discriminative local visual commonality and temporal consistency, is often neglected in the design of hash functions. In this paper, we propose a supervised method that explores the structure learning techniques to design efficient hash functions. The proposed video hashing method formulates a minimization problem over a structure-regularized empirical loss. In particular, the structure regularization exploits the common local visual patterns occurring in video frames that are associated with the same semantic class, and simultaneously preserves the temporal consistency over successive frames from the same video. We show that the minimization objective can be efficiently solved by an Accelerated Proximal Gradient (APG) method. Extensive experiments on two large video benchmark datasets (up to around 150K video clips with over 12 million frames) show that the proposed method significantly outperforms the state-of-the-art hashing methods.

Perceptual image hashing using center-symmetric local binary patterns

Abstract: Perceptual image hashing finds increasing attention in several multimedia security applications such as image identification/authentication, tamper detection, and watermarking. Robust feature extraction is the main challenge in hashing schemes. Local binary pattern (LBP) is a new feature which is due to its simplicity, discriminative power, computational efficiency, and robustness to illumination changes has been used in various image applications. In this paper, we propose a robust image hashing scheme using center-symmetric local binary patterns (CSLBP). In the proposed image hashing, CSLBP features are extracted from each non-overlapping block within the original gray-scale image. For each block, the final hash code is obtained by inner product of its CSLBP feature vector and a pseudorandom weight vector. Furthermore, singular value decomposition (SVD) is combined with CSLBP to introduce a more robust hashing method called SVD-CSLBP. Performances of the proposed hashing schemes are evaluated with two groups of popular applications in perceptual image hashing schemes: image identification and image authentication. Experimental results show that the proposed methods are robust to a wide range of distortions and attacks such as additive noise, blurring, brightness changes and JPEG compression. Moreover, the proposed methods have this capability to localize the tampering area, which is not possible in all hashing schemes.

Collision-Free Hashing from Lattice Problems

Abstract: In 1995, Ajtai described a construction of one-way functions whose security is equivalent to the difficulty of some well known approximation problems in lattices. We show that essentially the same construction can also be used to obtain collision-free hashing. This paper contains a self-contained proof sketch of Ajtai's result.