Lingyu Yan

Least square regularized spectral hashing for similarity search

Among the existing hashing methods, spectral hashing (SpH) and self-taught hashing (STH) are considered as the state-of-the-art works. However, two such methods still have some drawbacks. For example, when generating the extension of out-of-sample, SpH makes assumption that data follows uniform distribution but it is impractical. As to STH, its hash functions are obtained by training SVM classifier bit-by-bit, which will lead to ten-fold increase in training time. Moreover, they both suffer overfitting issue. To conquer those drawbacks, we propose a new hashing method, also called LS_SPH, which adopts a unified objective function to obtain the binary embeddings of training objects and hash functions for predicting hash code of test object. Integrating two such processes together will bring in two advantages: (1) It can highly decrease the time complexity of offline stage for training hash codes and hash function due to not requiring extra time for learning hash function. (2) The overfitting issue can be successfully avoided because the empirical loss function associated with hash function is served as the regularization item in objective function in this method. The extensive experiments show that the LS_SPH is superior to the state-of-the-art hashing methods such as SpH and STH on the whole.

Nonnegative sparse coding induced hashing for image copy detection

Among the existing hashing methods, the Self-taught hashing (STH) is regarded as the state-of-the-art work. However, it still suffers the problem of semantic loss, which mainly comes from the fact that the original optimization objective of in-sample data is NP-hard and therefore is compromised into the combination of Laplacian Eigenmaps (LE) and binarization. Obviously, the shape associated with the embedding of LE is quite dissimilar to that of binary code. As a result, binarization of the LE embedding readily leads to significant semantic loss. To overcome this drawback, we combine the constrained nonnegative sparse coding and the Support Vector Machine (SVM) to propose a new hashing method, called nonnegative sparse coding induced hashing (NSCIH). Here, nonnegative sparse coding is exploited for seeking a better intermediate representation, which can make sure that the binarization can be smoothly conducted. In addition, we build an image copy detection scheme based on the proposed hashing methods. The extensive experiments show that the NSCIH is superior to the state-of-the-art hashing methods. At the same time, this copy detection scheme can be used for performing copy detection over very large image database.

Fast image copy detection approach based on local fingerprint defined visual words

Recently the methods based on bag-of-visual words have become very popular in near-duplicate retrieval and content identification. However, obtaining the visual vocabulary by quantization is very time-consuming and unscalable to large databases. In this paper, we propose a fast copy detection method which uses local image fingerprints to define visual words. To construct the fingerprint, a 32-bit vector is extracted from the local description and then converted into a number which is used to define the visual word. Then, a histogram intersection is employed to measure the similarity between two images. Since the fingerprint building is of low-complexity, this method is very efficient and scalable to very large databases. Furthermore, the fingerprint-defined visual words are more discriminative and precise than the clustering-defined visual words because the vocabulary size could be large enough while maintaining high efficiency. Visual words with strong discriminability can distinguish copies from similar objects, which can reduce the number of false positives and improve the precision and efficiency. The evaluation shows that our approach significantly outperforms state-of-the-art methods.

Nonnegative sparse locality preserving hashing

It is a NP-hard problem to optimize the objective function of hash-based similarity search algorithms, such as Spectral Hashing and Self-Taught Hashing. To make the problem solvable, existing methods have relaxed the constraints on hash codes from binary values (discrete) to real values (continuous). Then eigenvalue decomposition was employed to achieve the relaxed real solution. The main problem is that the signs of the relaxed continuous solution are mixed. Such results may deviate severely from the true solution, which has lead to significant semantic loss. Moreover, eigenvalue decomposition confronts singularity problem when the dimension of the data is larger than the sample size. To address these problems, we propose a novel method named Nonnegative Sparse Locality Preserving Hashing (NSLPH). Nonnegative and sparse constraints are imposed for a more accurate solution which preserves semantic information well. Then, we have applied nonnegative quadratic programming and multiplicative updating to solve the optimization problem, which successfully avoids the singularity problem of the eigenvalue decomposition. The extensive experiments presented in this paper demonstrate that the proposed approach outperforms the state-of-the-art algorithms.

Inductive Transfer Deep Hashing for Image Retrieval

With the explosive increase of online images, fast similarity search is increasingly critical for large scale image retrieval. Several hashing methods have been proposed to accelerate image retrieval, a promising way is semantic hashing which designs compact binary codes for a large number of images so that semantically similar images are mapped to similar codes. Supervised methods can handle such semantic similarity but they are prone to overfitting when the labeled data is few or noisy. In this paper, we concentrate on this issue and propose a novel Inductive Transfer Deep Hashing (ITDH) approach for semantic hashing based image retrieval. A transfer deep learning algorithm has been employed to learn the robust image representation, and the neighborhood-structure preserved method has been used to mapped the image into discriminative hash codes in hamming space. The combination of the two techniques ensures that we obtain a good feature representation and a fast query speed without depending on large amounts of labeled data. Experimental results demonstrate that the proposed approach is superior to some state-of-the-art methods.

Kernelized Neighborhood Preserving Hashing for Social-Network-Oriented Digital Fingerprints

Digital fingerprinting is a promising approach to protect multimedia content from unauthorized redistribution. However, the existing fingerprints are unsuitable for social network tasks, because they fail to represent the social network structure, which incurs inefficient fingerprint coding. In addition, they are infeasible to efficiently trace colluders due to the large scale of social networks. To address these problems, we design a novel fingerprint, which consists of community relationship code and user identification code. Aiming to preserving the social network structure, we propose a kernelized neighborhood preserving hashing method to generate community relationship codes. The proposed method assigns similar community relationship codes to users in the same or close communities, which improves the anticollusion performance. Because the community relationship codes are binary and neighborhood preserving, they can be used for fast indexing and retrieval. To accelerate the collusion fingerprint tracing, we treat the community relationship codes as index keys to construct a hash table and an inverted index table. Based on the tables, we correspondingly propose an efficient fingerprint detection method. Extensive experiments show that the proposed fingerprint is suitable for social network tasks and the real colluders can be efficiently identified by the proposed fingerprint detection approach.

PM-DFT: A New Local Invariant Descriptor Towards Image Copy Detection

Currently, global-features-based image copy detection is vulnerable to geometric transformations like cropping, shift, and rotations. To resolve this problem, some algorithms based on local descriptors have been proposed. However, the local descriptors, which were originally designed for object recognition, are not suitable for copy detection because they cause the problems of false positives and ambiguities. Instead of relying on the local gradient statistic as many existing descriptors do, we propose a new invariant local descriptor based on local polar-mapping and discrete Fourier transform. Then based on this descriptor, we propose a new framework of copy detection, in which virtual prior attacks and attack weight are employed for training and selecting only a few robust features. This consequently improves the storage and detection efficiency. In addition, it is worth noting that the feature matching takes the locations and orientations of interest points into consideration, which increases the number of matched regions and improves the recall. Experimental results show that the new descriptor is more robust and distinctive, and the proposed copy detection scheme using this descriptor can substantially enhance the accuracy and recall of copy detection and lower the false positives and ambiguities.

Feature fusion based hashing for large scale image copy detection

Most of existing approaches use only a single feature to represent an image for copy detection. However, a single feature is often insufficient to characterize the image content. Besides, with the exponential growth of online images, its urgent to explore a way of tackling the problem of large scale. In this paper, we propose a feature fusion based hashing method which effectively utilize the correlation between two feature models and efficiently accomplish large scale image copy detection. To accurately map images into the Hamming space, our hashing method not only preserves the local structure of individual feature but also globally consider the local structures for all the features to learn a group of hash functions. The experiment results show that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency.

Neighborhood preserving hashing for fast similarity search

Fast similarity search methods are increasingly critical for many large-scale learning tasks, particularly in the communities of machine learning and data mining. Recently, data-aware hashing method is regarded as a promising approach for similarity search which maps high-dimensional feature vectors into efficient and compact hash codes while preserving the corresponding neighborhood structure. Although some recent hashing methods based on eigenvalue decomposition perform well, they suffer from semantic loss. In this paper, we concentrate on this issue and propose a novel neighborhood preserving hashing approach which adopts a brand-new method to combine non-negative matrix factorization and locality linear embedding without introducing any additional parameter. The combination of these two classical techniques ensures that we obtain a parts-based representation which not only fulfill the psychological and physiological requirements of human perception but also conserve the intrinsic neighborhood structure of the original data. Experiments are conducted to demonstrate that the proposed approach is superior to some state-of-the-art methods.

Local and global structure preserving hashing for fast digital fingerprint tracing

Digital fingerprinting is a promising approach to protect multimedia contents from unauthorized redistribution. Whereas, large scale and high dimensionality make existing fingerprint detection methods fail to trace the traitors efficiently. To handle this problem, we propose a novel local and global structure preserving hashing to conduct fast fingerprint detection. This is the first work that introduces hash-based similarity search method to perform fingerprint detection. Applying the hashing method, we obtain a neighborhood-preserving low-dimensional representation (e. g. hash code) for each fingerprint. Through hash codes, we can find the nearest neighbors of the extracted fingerprint, thereby tracing the real traitors within a small range. Preserving the local structure facilitates to find the nearest neighbors of the query fingerprint efficiently, and preserving the global structure ensures hash codes of fingerprints as discriminative as possible. These properties make the proposed approach efficient to trace the real traitors. Extensive experiments demonstrate that the proposed approach outperforms traditional linear scan detection methods in term of efficiency.