List of Articles Sparse Coding

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  1 - A new Sparse Coding Approach for Human Face and Action Recognition
  Mohsen Nikpoor Mohammad Reza Karami-Mollaei Reza Ghaderi
  10.7508/jist.2017.17.003
  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 More

  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. Manuscript profile
• Open Access Article
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  2 - Visual Distractors Detecting in Images Using Weighted Two Phase Test Sample Sparse Representation Method
  F. Sabouri F. yaghmaee
  The image observer usually wants to receive the message and the main subject of the image in the shortest time. Hence, assuming there is useful information in the salient regions, the human vision system unconsciously guides visual attention towards them. This assumptio More

  The image observer usually wants to receive the message and the main subject of the image in the shortest time. Hence, assuming there is useful information in the salient regions, the human vision system unconsciously guides visual attention towards them. This assumption is not always correct in practice, and in some cases, salient regions merely cause visual distractions. Therefore, in different applications, a mechanism is needed to identify these regions. To prevent from distracting observer’s attention from the main subject, these regions are eliminated. Furthermore, neglecting these regions could be of considerable assistance to the methods that function base on salient regions recognition. So, in this paper, Based on the methods of the class imbalance challenge each segment of training images in the dataset is a partition to 9 classes according to the relevant mask in the dataset, that the number of each class is proportional to its disturbance intensity. Then, segment-based features are extracted and determining the class of each segment is determined according to WTPTSSR method, which is based on the Sparse Coding and Representation system.Finally, in order to precisely analyzing the proposed method and comparing it to other approaches, four analysis criteria with different performances are presented. According to results, despite being time-consuming, the proposed method has a higher accuracy than the previous ones. Manuscript profile