Yigang Cen

Histogram of maximal optical flow projection for abnormal events detection in crowded scenes

Abnormal events detection plays an important role in the video surveillance, which is a challenging subject in the intelligent detection. In this paper, based on a novel motion feature descriptor, that is, the histogram of maximal optical flow projection (HMOFP), we propose an algorithm to detect abnormal events in crowded scenes. Following the extraction of the HMOFP of the training frames, the one-class support vector machine (SVM) classification method is utilized to detect the abnormality of the testing frames. Compared with other methods based on the optical flow, experiments on several benchmark datasets show that our algorithm is effective with satisfying results.

Abnormal event detection based on sparse reconstruction in crowded scenes

In this paper, we propose an algorithm of abnormal event detection in crowded scenes using sparse representation over the bases of normal motion feature descriptors. To construct an over-complete dictionary, we extract the histogram of maximal optical flow projection (HMOFP) feature from a set of normal training frames. Then the K-SVD dictionary training method is used to get a redundant dictionary after a process of selecting the training samples, which is better than the dictionary simply composed by the HMOFP feature of the whole training frames. In order to detect whether a frame is normal or not, we use the U-norm of the sparse reconstruction coefficients (i.e., the sparse reconstruction cost, SRC) to show the anomaly of the testing frame, which is simple but very effective. The experiment results on UMN dataset and the comparison to the state-of-the-art methods show that our algorithm is promising.

Video restoration based on PatchMatch and reweighted low-rank matrix recovery

In this paper, a new video restoration approach is proposed. By using a modified version of random PatchMatch algorithm, nearest-neighbor patches among the video frames can be grouped quickly and accurately. Then the video restoration problem can be boiled down to a low-rank matrix recovery problem, which is able to separate sparse errors from matrices that possess potential low-rank structures. Furthermore, the reweighted low-rank matrix model is used to improve the performance of video restoration by enhancing the sparsity of the sparse matrix and the low-rank property of the low-rank matrix. Experimental results show that our system achieves good performance in denosing of joint multi-frames and inpainting in the presence of small damaged areas.

Separable vocabulary and feature fusion for image retrieval based on sparse representation

Visual vocabulary is the core of the Bag-of-visual-words (BOW) model in image retrieval. In order to ensure the retrieval accuracy, a large vocabulary is always used in traditional methods. However, a large vocabulary will lead to a low recall. In order to improve recall, vocabularies with medium sizes are proposed, but they will lead to a low accuracy. To address these two problems, we propose a new method for image retrieval based on feature fusion and sparse representation over separable vocabulary. Firstly, a large vocabulary is generated on the training dataset. Secondly, the vocabulary is separated into a number of vocabularies with medium sizes. Thirdly, for a given query image, we adopt sparse representation to select a vocabulary for retrieval. In the proposed method, the large vocabulary can guarantee a relatively high accuracy, while the vocabularies with medium sizes are responsible for high recall. Also, in order to reduce quantization error and improve recall, sparse representation scheme is used for visual words quantization. Moreover, both the local features and the global features are fused to improve the recall. Our proposed method is evaluated on two benchmark datasets, i.e., Coil20 and Holidays. Experiments show that our proposed method achieves good performance.

Analytic separable dictionary learning based on oblique manifold

Sparse representation based on dictionary has gained increasing interest due to its extensive applications. Because of the disadvantages of computational complexity of traditional dictionary learning, we propose an algorithm of analytic separable dictionary learning. Considering the differences of sparse coefficient matrix and dictionary, we divide our algorithm into two phases: 2D sparse coding and dictionary optimization. Then an alternative iteration method is used between these two phases. The algorithm of 2D-OMP (2-dimensional Orthogonal Matching Pursuit) is used in the first phase because of its low complexity. In the second phase, we create a continuous function of the optimization problem, and solve it by the conjugate gradient method on oblique manifold. By employing the separable structure of the optimized dictionary, a competitive result is achieved in our experiments for image de-noising.

Multi-separable dictionary learning

Abstract As the extensive applications of sparse representation, the methods of dictionary learning have received widespread attentions. In this paper, we propose a multi-separable dictionary learning (MSeDiL) algorithm for sparse representation, which is based on the Lagrange Multiplier and the QR decomposition. Different with the traditional dictionary learning methods, the training samples are clustered firstly. Then the separable dictionaries for each cluster are optimized by the QR decomposition. The efficiency of the reconstruction process is improved in our algorithm because of the under-determinedness of the dictionaries for each cluster. Experimental results show that with the similar PSNR (Peak Signal to Noise Ratio) and SSIM (Structure Similarity Index), the reconstruction speed of our algorithm is much faster than other dictionary learning methods, especially when the size of samples is large.

Fast smooth rank function approximation based on matrix tri-factorization

Abstract Recently, Smooth Rank Function (SRF) is proposed for matrix completion problem. The main idea of this algorithm is based on a continuous and differentiable approximation of the rank function. However, it need to deal with singular value decomposition of matrix in each iteration, which consumes much time for large matrix. In this paper, by utilizing the tri-factorization of matrix, a fast matrix completion method based on SRF is proposed. Then, based on our fast matrix completion method, a rank adaptive smooth rank function approximation is presented with appropriate rank estimation. We mathematically prove the convergence of the proposed method. Experimental results show that our proposed method improves the running time significantly. Furthermore, our proposed method outperforms other existing matrix completion approaches in most cases.

Anomaly detection using sparse reconstruction in crowded scenes

In this paper, we propose an algorithm of anomaly detection in crowded scenes by using sparse representation over the normal bases. First, the histogram of maximal optical flow projection (HMOFP) features are extracted from a set of normal training data. Then, the online dictionary learning algorithm is used to train an optimal dictionary with proper redundancy, which is better than the dictionary simply composed by the HMOFP features of the whole training data. In order to detect the normalness of a frame, the l1-norm of the sparse reconstruction coefficients is used as the Reconstruction Coefficient Sparsity (RCS). Our algorithm is effective for both global abnormal events (GAE) and local abnormal events (LAE). We evaluate our method on three benchmark datasets-the UMN dataset, the PETS2009 dataset and the UCSD Ped1 dataset. Compared with the most popular methods, experimental results show that our algorithm achieves good results especially for the pixel-level local abnormal event localization.

SURF binarization and fast codebook construction for image retrieval

Abstract A new framework for image retrieval/object search is proposed based on the VLAD model and SURF descriptors to improve the codebook construction speed, the image matching accuracy, and the online retrieval speed and to reduce the data storage. First, SURF binarization and dimensionality reduction methods are proposed to convert a 64-dimensional SURF descriptor into an 8-dimensional descriptor. Second, a two-step clustering algorithm is proposed for codebook construction to significantly reduce the computational cost of clustering while maintaining the accuracy of the clustering results. Moreover, for object search, a scalable overlapping partition method is proposed to segment an image into 65 patches with different sizes so that the object can be matched quickly and efficiently. Finally, a feature fusion strategy is employed to compensate the performance degradation caused by the information loss of our proposed dimensionality reduction method. Experiments on the Holidays and Oxford datasets demonstrate the effectiveness and efficiency of the proposed algorithms.

An Operation Reduction Using Fast Computation of an Iteration-Based Simulation Method with Microsimulation-Semi-Symbolic Analysis

This paper presents a method for shortening the computation time and reducing the number of math operations required in complex calculations for the analysis, simulation, and design of processes and systems. The method is suitable for education and engineering applications. The efficacy of the method is illustrated with a case study of a complex wireless communication system. The computer algebra system (CAS) was applied to formulate hypotheses and define the joint probability density function of a certain modulation technique. This innovative method was used to prepare microsimulation-semi-symbolic analyses to fully specify the wireless system. The development of an iteration-based simulation method that provides closed form solutions is presented. Previously, expressions were solved using time-consuming numerical methods. Students can apply this method for performance analysis and to understand data transfer processes. Engineers and researchers may use the method to gain insight into the impact of the parameters necessary to properly transmit and detect information, unlike traditional numerical methods. This research contributes to this field by improving the ability to obtain closed form solutions of the probability density function, outage probability, and considerably improves time efficiency with shortened computation time and reducing the number of calculation operations.