Personal identification based on vein pattern is one of the latest biometric approaches that have attracted lots of attention. Besides, Convolutional Sparse Coding (CSC) is a popular model in the signal and image processing communities, resolving some limitations of the traditional patch-based Sparse representations. As most existing CSC algorithms are suited for image restoration, we present a novel discriminative model based on CSC for dorsal hand vein recognition in this paper. The proposed method, discriminative local block coordinate descent (D-LoBCoD), is based on extending the LoBCoD algorithm by incorporating the classification error into the objective function that considers the performance of a linear classifier and the representational power of the filters simultaneously. Thus, for training, in each iteration, after updating the Sparse coefficients and convolutional filters, we minimize the classification error by updating the classifier’s parameters according to the label information. Finally, after training, the label of the query image will be determined by the trained classifier. One thousand two hundred dorsal hand vein images taken from 100 individuals are used to verify the validity of the proposed methods. The experimental results show that our method outperforms other competing methods. Further, we demonstrate that our proposed method is less dependent on the number of training samples because of capturing more representative information from the corresponding images.

Due to the growing increase of generated images via cameras and various instruments, image processing has found an important role in most of practical usages including medical, security and driving. However, most of the available models has no considerable performance and in some usages the amount of error is very effective. The main cause of this failure in most of available models is the distribution mismatch across the source and target domains. In fact, the made model has no generalization to test data with different properties and distribution compared to the source data, and its performance degrades dramatically to face with new data. In this paper, we propose a novel approach entitled Sparse Coding and ADAptive classification (SADA) which is robust against data drift across domains. The proposed model reduces the distribution difference across domains via generating a common subspace between the source and target domains and increases the performance of model. Also, SADA reduces the distribution mismatch across domains via the selection of the source samples which are related to target samples. Moreover, SADA adapts the model parameters to build an adaptive model to encounter with data drift. Our variety of experiments demonstrate that the proposed approach outperforms all stat-of-the-art domain adaptation methods.

Analyzing motion patterns in traffic videos can be exploited directly to generate highlevel descriptions of the video contents. Such descriptions may further be employed in different traffic applications such as traffic phase detection and abnormal event detection. One of the most recent and successful unsupervised methods for complex traffic scene analysis is based on topic models. In this paper, a two-level Sparse Topical Coding (STC) topic model is proposed to analyze traffic surveillance video sequences which contain hierarchical patterns with complicated motions and co-occurrences. The first level STC model is applied to automatically cluster optical flow features into motion patterns. Then, the second level STC model is used to cluster motion patterns into traffic phases. Experiments on a real world traffic dataset demonstrate the effectiveness of the proposed method against conventional onelevel topic model based methods. The results show that our two-level STC can successfully discover not only the lower level activities but also the higher level traffic phases, which makes a more appropriate interpretation of traffic scenes. Furthermore, based on the two-level structure, either activity anomalies or traffic phase anomalies can be detected, which cannot be achieved by the one-level structure.

Since the anomalies are unknown targets with low probabilities of occurrence which are significantly different from their neighbors، anomaly detection could be considered as one of the most important information extraction approaches from hyperspectral data. Various types of parametric and non-parametric algorithms have been developed in this area from the 1990's decade. Recently، Sparse representation methods have been introduced and successfully accepted as a useful tool for anomaly detection based on the recovery of the majority of high-dimensional signals via a low-dimensional subspace through a dictionary of normalized signals called atoms. In other words، having a dictionary composed of bases denoting the background subspace enables the accurate recovery of background signals. Moreover، the presence of anomaly signals، assuming their deviation from the background subspace، will not have a precise estimation by the background dictionary. Hence the main idea of these anomaly detection methods is focused on evaluating recovery errors of signals by a dictionary that describes the background subspace. In such procedure، removing the atoms that describe the anomaly in the background dictionary can be considered as one of the essential actions. To this aim making diversity in the definition of spatial neighborhoods of spectral signals، as well as voting-based judgment in different situations of the spatial distribution could be proposed. In other words، by designing an optimized local dictionary، based on a local sliding window، the votes of each signal in terms of anomaly presence in each spatial neighborhood could be calculated with the aim of achieving better judgment. In this paper، a new anomaly detector for hyperspectral images is proposed based on simultaneous Sparse representation using a new structured sliding window. The main contribution of this research is to improve the judgments about the anomaly presence probability using information collected during transition of the mentioned sliding window for each pixel under test. In this algorithm، each pixel experiences various spatial positions with respect to the neighbors through the transition of the sliding window. In each position، an optimized local background dictionary is molded using a well-known K-SVD method as an iterative process and the recovery error of Sparse Coding for each pixel under test is calculated using a simultaneous orthogonal matching pursuit algorithm (SOMP). So، the votes of each pixel in terms of the anomaly presence in each neighborhood are calculated and finally the variance of these estimated errors is considered as the anomaly detection criterion. The experimental results of the proposed method using four datasets (synthetic and real datasets) proved its higher performance compared to the GRX، LRX، CRD and BJSR detectors with an average efficiency improvement of about 9%. In addition automatic tuning of the proposed algorithm parameters (level of sparsity and the size of sliding window) and developing parallel processing techniques to improve the running time of this algorithm are the focus of our future research. It is notable that the implementation of this idea and its success showed that development of voting algorithms and the combination of the results could be considered as an efficient approach could also be utilized in other hyperspectral image processing algorithms.

The progress of communications over internet media such as social media and messengers has led to the production of large amount of textual data. This kind of information contains a lot of valuable knowledge and can be used to improve the performance of other natural language processing (NLP) tasks. There are several ways to use such information, of which one is text summarization. Summarizing textual information can extract the main content of text within a short time. In this paper, we propose an approach for extractive summarization on Persian texts by using sentences embedding and a Sparse Coding framework. Most previous works focuses on text’s sentences individually which may not consider the hidden structure patterns between them. In this paper, our proposed approach can consider the relations between the text’s sentences by using three main criteria, namely coverage, diversity and sparsity, when selecting the summary sentences. By considering these criteria, we select sentences that can reconstruct the whole text with least reconstruction error. The proposed approach is evaluated on Persian dataset Pasokh and achieved 10. 02% and 8. 65% improvement compared to the state-of-the-art methods in rouge-1 and rouge-2 f-scores, respectively. We show that considering semantic relations among the text’s sentences can lead us to better sentence summarization.

The progress of communications over internet media such as social media and messengers has led to the production of large amount of textual data. This kind of information contains a lot of valuable knowledge and can be used to improve the performance of other natural language processing (NLP) tasks. There are several ways to use such information, of which one is text summarization. Summarizing textual information can extract the main content of text within a short time. In this paper, we propose an approach for extractive summarization on Persian texts by using sentences embedding and a Sparse Coding framework. Most previous works focuses on text’ s sentences individually which may not consider the hidden structure patterns between them. In this paper, our proposed approach can consider the relations between the text’ s sentences by using three main criteria, namely coverage, diversity and sparsity, when selecting the summary sentences. By considering these criteria, we select sentences that can reconstruct the whole text with least reconstruction error. The proposed approach is evaluated on Persian dataset Pasokh and achieved 10. 02% and 8. 65% improvement compared to the state-of-theart methods in rouge-1 and rouge-2 f-scores, respectively. We show that considering semantic relations among the text’ s sentences can lead us to better sentence summarization.

Sparse network Coding was introduced to reduce the computational complexity of the random linear network Coding. In this method, most of the deCoding matrix coefficients are zero. Partial deCoding means the possibility of deCoding a part of the raw packets is one of the capabilities of the Sparse network Coding method. We introduce three different models of Sparse Coding method as an approach to reduce deCoding latency in real-time communication. More precisely, we first evaluate a Sparse network Coding for a no feedback configuration in terms of the performance of the total number of transmissions required, and the average packet deCoding delay for a generation of raw packets, by introducing a Markov chain-based model. Then we evaluate the accuracy of the proposed model using extensive simulation and show that the proposed model can accurately estimate the number of required transmissions and deCoding delay for a generation of packets. The results also evaluate the accuracy of the model in the erasure channel. In the following, we introduce the feedback-based model and we show that this model can create a better balance between the functions of the number of transmissions and the average deCoding delay per packet. Finally, by focusing on the problem of finding the random spanning tree, we present a graph-based model for analyzing Sparse network Coding and show that although the proposed model is valid only for grade 2 sparsity, it also has the capacity to develop for lower sparsity.

Classification of various rice types and determination of its quality is a major issue in the scientific and commercial fields associated with modern agriculture. In recent years, various image processing techniques are used to identify different types of agricultural products. There are also various color and texture-based features in order to achieve the desired results in this area. In this paper, the problem of rice categorization and quality detection using compressive sensing concepts is considered. This issue includes Sparse representation and dictionary learning techniques to achieve over-complete models and represent the structural content of rice variety. Also, dictionaries are learned in such a way to have the least coherence values to each other. The results of the proposed classifier based on the learned models are compared with the results obtained from the neural network and support vector machine classifiers. Simulation results show that the proposed method based on the combinational features is able to identify the type of rice grain and determine its quality with high accuracy rate.

Sparse Coding is an unsupervised method which learns a set of over-complete bases to represent data such as image, video and etc. In the cases where we have some similar images from the different classes, using the Sparse Coding method the images may be classified into the same class and devalue classification performance. In this paper, we propose an Affine Graph Regularized Sparse Coding approach for resolving this problem. We apply the Sparse Coding and graph regularized Sparse Coding approaches by adding the affinity constraint to the objective function to improve the recognition rate. Several experiments has been done on well-known face datasets such as ORL and YALE. The first experiment has been done on ORL dataset for face recognition and the second one has been done on YALE dataset for face expression detection. Both experiments have been compared with the basic approaches for evaluating the proposed method. The simulation results show that the proposed method can significantly outperform previous methods in face classification. In addition, the proposed method is applied to KTH action dataset and the results show that the proposed Sparse Coding approach could be applied for action recognition applications too.

Due to the development of social networks and the Internet of things, we recently have faced with large datasets. High-dimensional data is mixed with redundant and irrelevant features, so the performance of machine learning methods is reduced. Feature selection is a common way to tackle this issue with aiming of choosing a small subset of relevant and non-redundant features. Most of the existing feature selection works are for supervised applications, which assume that the information of class labels is available. While in many real-world applications, it is not possible to provide complete knowledge of class labels. To overcome this shortcoming, an unsupervised feature selection method is proposed in this paper. The proposed method uses the matrix factorization-based regularized self-representation model to weight features based on their importance. Here, we initialize the weights of features based on the correlation among features. Several experiments are performed to evaluate the effectiveness of the proposed method. Then the results are compared with several baselines and state-of-the-art methods, which show the superiority of the proposed method in most cases.