Xin-Shun Xu

Optimized Cartesian K-Means

Product quantization-based approaches are effective to encode high-dimensional data points for approximate nearest neighbor search. The space is decomposed into a Cartesian product of low-dimensional subspaces, each of which generates a sub codebook. Data points are encoded as compact binary codes using these sub codebooks, and the distance between two data points can be approximated efficiently from their codes by the precomputed lookup tables. Traditionally, to encode a subvector of a data point in a subspace, only one sub codeword in the corresponding sub codebook is selected, which may impose strict restrictions on the search accuracy. In this paper, we propose a novel approach, named optimized cartesian K-means (ock-means), to better encode the data points for more accurate approximate nearest neighbor search. In ock-means, multiple sub codewords are used to encode the subvector of a data point in a subspace. Each sub codeword stems from different sub codebooks in each subspace, which are optimally generated with regards to the minimization of the distortion errors. The high-dimensional data point is then encoded as the concatenation of the indices of multiple sub codewords from all the subspaces. This can provide more flexibility and lower distortion errors than traditional methods. Experimental results on the standard real-life data sets demonstrate the superiority over state-of-the-art approaches for approximate nearest neighbor search.

Ensemble multi-instance multi-label learning approach for video annotation task

Automatic video annotation is an important ingredient for video indexing, browsing, and retrieval. Traditional studies represent one video clip with a flat feature vector; however, video data usually has natural structure. Moreover, a video clip is generally relevant to multiple concepts. Indeed, the video annotation task is inherently a Multi-Instance Multi-Label (MIML) learning problem. In this paper, we propose the En-MIMLSVM approach for the video annotation task. It considers the class imbalance and long time training problems of most video annotation tasks. In addition, a temporally consistent weighted multi-instance kernel is developed to take into account both the temporal consistency in video data and the significance of instances of different levels in pyramid representation. The En-MIMLSVM is evaluated on TRECVID 2005 data set, and the results show that it outperforms several state-of-the-art methods.

Letter: An efficient simulated annealing algorithm for the minimum vertex cover problem

The minimum vertex cover problem is a classic graph optimization problem. It is well known that it is an NP-complete problem. In this paper, an efficient simulated annealing algorithm is presented for the minimum vertex cover problem. In this algorithm, an acceptance function is defined for every vertex. This can help the algorithm in finding a near-optimal solution to a problem. Simulations are performed on several benchmark graphs, and the simulation results show that the proposed algorithm provides a high probability of finding optimal solutions.

Linear unsupervised hashing for ANN search in Euclidean space

Approximate nearest neighbors (ANN) search for large scale data has attracted considerable attention due to the fact that large amounts of data are easily available. Recently, hashing has been widely adopted for similarity search because of its good potential for low storage cost and fast query speed. Among of them, when semantic similarity information is available, supervised hashing methods show better performance than unsupervised ones. However, supervised hashing methods need explicit similarity information which is not available in some scenarios. In addition, they have the problems of difficult optimization and time consuming for training, which make them unpracticable to large scale data. In this paper, we propose an unsupervised hashing method - Unsupervised Euclidean Hashing (USEH), which learns and generates hashing codes to preserve the Euclidean distance relationship between data. Specifically, USEH first utilizes Locality-Sensitive Hashing (LSH) to generate pseudo labels; then, it adopts a sequential learning strategy to learn the hash functions, one bit at a time, which can generate very discriminative codes. Moreover, USEH avoids explicitly computing the similarity matrix by decomposing it into the product of a label matrix and its transposition, which makes the training complexity of USEH linear to the size of training samples when the number of training samples is much greater than the dimension of feature. Thus, it can efficiently work on large scale data. We test USEH on two large scale datasets - SIFT1M and GIST1M. Experimental results show that USEH is comparable to state-of-the-art unsupervised hashing methods.

Ensemble approach based on conditional random field for multi-label image and video annotation

Multi-label image/video annotation is a challenging task that allows to correlate more than one high-level semantic keyword with an image/video-clip. Previously, a single model is usually used for the annotation task, with relatively large variance in performance. The correlation among the annotation keywords should also be considered. In this paper, to reduce the performance variance and exploit the correlation between keywords, we propose the En-CRF (Ensemble based on Conditional Random Field) method. In this method, multiple models are first trained for each keyword, then the predictions of these models and the correlations between keywords are incorporated into a conditional random field. Experimental results on benchmark data set, including Corel5k and TRECVID 2005, show that the En-CRF method is superior or highly competitive to several state-of-the-art methods.

Semi-supervised multi-instance multi-label learning for video annotation task

Traditional approaches for automatic video annotation usually represent one video clip with a flat feature vector, neglecting the fact that video data contain natural structures. It is also noteworthy that a video clip is often relevant to multiple concepts. Indeed, the video annotation task is inherently a Multi-Instance Multi-Label learning (MIML) problem. Considering that manually annotating videos is labor-intensive and time-consuming, this paper proposes a semi-supervised MIML approach, SSMIML, which is able to exploit abundant unannotated videos to help improve the annotation performance. This approach takes label correlations into account, and enforces similar instances to share similar multi-labels. Evaluation on TREVID 2005 show that the proposed approach outperforms several state-of-the-art methods.

Letter: A method to improve the transiently chaotic neural network

Abstract In this article, we propose a method for improving the transiently chaotic neural network (TCNN) by introducing several time-dependent parameters. This method allows the network to have rich chaotic dynamics in its initial stage and to reach a state in which all neurons are stable soon after the last bifurcation. This enables the network to have rich search ability initially and to use less CPU time to reach a stable state. The simulation results on the N-queen problem confirm that this method effectively improves both the solution quality and convergence speed of TCNN.

Supervised Robust Discrete Multimodal Hashing for Cross-Media Retrieval

Recently, multimodal hashing techniques have received considerable attention due to their low storage cost and fast query speed for multimodal data retrieval. Many methods have been proposed; however, there are still some problems that need to be further considered. For example, some of these methods just use a similarity matrix for learning hash functions which will discard some useful information contained in original data; some of them relax binary constraints or separate the process of learning hash functions and binary codes into two independent stages to bypass the obstacle of handling the discrete constraints on binary codes for optimization, which may generate large quantization error; some of them are not robust to noise. All these problems may degrade the performance of a model. To consider these problems, in this paper, we propose a novel supervised hashing framework for cross-modal retrieval, i.e., Supervised Robust Discrete Multimodal Hashing (SRDMH). Specifically, SRDMH tries to make final binary codes preserve label information as same as that in original data so that it can leverage more label information to supervise the binary codes learning. In addition, it learns hashing functions and binary codes directly instead of relaxing the binary constraints so as to avoid large quantization error problem. Moreover, to make it robust and easy to solve, we further integrate a flexible l2,p loss with nonlinear kernel embedding and an intermediate presentation of each instance. Finally, an alternating algorithm is proposed to solve the optimization problem in SRDMH. Extensive experiments are conducted on three benchmark data sets. The results demonstrate that the proposed method (SRDMH) outperforms or is comparable to several state-of-the-art methods for cross-modal retrieval task.

Dictionary Learning Based Hashing for Cross-Modal Retrieval

Recent years have witnessed the growing popularity of cross-modal hashing for fast multi-modal data retrieval. Most existing cross-modal hashing methods project heterogeneous data directly into a common space with linear projection matrices. However, such scheme will lead to large error as there will probably be some heterogeneous data with semantic similarity hard to be close in latent space when linear projection is used. In this paper, we propose a dictionary learning cross-modal hashing (DLCMH) to perform cross-modal similarity search. Instead of projecting data directly, DLCMH learns dictionaries and generates sparse representation for each instance, which is more suitable to be projected to latent space. Then, it assumes that all modalities of one instance have identical hash codes, and gets final binary codes by minimizing quantization error. Experimental results on two real-world datasets show that DLCMH outperforms or is comparable to several state-of-the-art hashing models.

Letter: A discrete competitive Hopfield neural network for cellular channel assignment problems

Abstract In this paper, we propose a discrete competitive Hopfield neural network (DCHNN) for the cellular channel assignment problem (CAP). The DCHNN can always satisfy the problem constraint and therefore guarantee the feasibility of the solutions for the CAP. Furthermore, the DCHNN permits temporary energy increases to escape from local minima by introducing stochastic dynamics. Simulation results show that the DCHNN has superior ability for the CAP within reasonable number of iterations.