Atta-ur-Rahman, I. Qureshi, M. Salam, M. Z. Muzaffar
{"title":"Adaptive communication using softcomputing techniques","authors":"Atta-ur-Rahman, I. Qureshi, M. Salam, M. Z. Muzaffar","doi":"10.1109/SOCPAR.2013.7054131","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054131","url":null,"abstract":"Adaptive communication has gained attention of almost every recent communication system because of its rate enhancement and context aware features. In this concept, different transmission parameters like transmit power, forward error correcting (FEC) code rate and modulation scheme are adaptively chosen according to the channel state information. Consequently, that set of transmission parameters is chosen that maximizes the channel capacity as well as fulfills the power and bit error rate constraints. Finding the optimum value of the said parameters is a highly non-linear problem with huge search space for solution. In this paper, we have investigated Ant Colony Optimization (ACO) in conjunction with a fuzzy rule base system (SA-FRBS) for adaptive coding, modulation and power in an orthogonal frequency division multiplexing environment. Proposed scheme is compared with Simulated Annealing and FRBS (SA-FRBS) assisted adaptive coding modulation and power scheme as well as with the fixed power scheme. Superiority of proposed scheme is shown by the simulations.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127342930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vu Lam, Duy-Dinh Le, Sang Phan Le, S. Satoh, Due Arm Duong, T. Ngo
{"title":"Evaluation of low-level features for detecting violent scenes in videos","authors":"Vu Lam, Duy-Dinh Le, Sang Phan Le, S. Satoh, Due Arm Duong, T. Ngo","doi":"10.1109/SOCPAR.2013.7054129","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054129","url":null,"abstract":"Automatically detecting violent scenes in videos not only has great potential in several applications (such as movie selection or recommendation for children) but also is a very hot academic research topic. Since 2011, violent scene detection task is one of the core tasks of MediaEval, a benchmarking initiative dedicated to evaluating new algorithms for multimedia access and retrieval1. In this paper, we evaluate the performance of low-level audio/visual features for the violent scene detection task using the datasets and evaluation protocol provided by the MediaEval organizers. Our result report can be used as a baseline for comparison of new algorithms in this task.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124174955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Stacking of texture based filters for visual place categorization","authors":"Nur Nabilah Abu Mangshor, A. Abdullah","doi":"10.1109/SOCPAR.2013.7054158","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054158","url":null,"abstract":"Recent research in computer vision has shown that combining multiple features is an effective way to improve classification performance. Furthermore, the use of filters that convolve images at multiple filter responses can increase descriptions to images. The more distinctive the filter responses, the better it able to distinguish characteristics from other groups. Thus, this paper describes a combination method that combines multiple classifier outputs at several filter responses to enhance the automatic visual place categorization system. Besides, one of the goals of this study is to explore performance differences between single and dedicated combination of filter response classifier methods. One possible problem of combining multiple filter responses for describing images is that the input vector becomes very large in dimensionality, which can increase the problem of overfitting and hinder generalization performance. Therefore, the stacking of support vector machine is used to compute the right output class from each single descriptor of filter responses that has been trained at the first layer of support vector machine. Next, the second layer support vector machine is used to combine those class probability output values of all trained first layer support vector models to learn the right output class. We have performed experiments on five different categories of visual places from the KTH-IDOL2 dataset with a single descriptor using 25 different filter responses of Laws filters. Results showed that the 2-layer stacking algorithm outperform the single and naive approaches that uses single filter response input vector and combines all filter response outputs directly in a very large single input vector respectively.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128612255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vu-Hoang Nguyen, T. Ngo, Khang Nguyen, D. Duong, Kien Nguyen, Duy-Dinh Le
{"title":"Re-ranking for person re-identification","authors":"Vu-Hoang Nguyen, T. Ngo, Khang Nguyen, D. Duong, Kien Nguyen, Duy-Dinh Le","doi":"10.1109/SOCPAR.2013.7054148","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054148","url":null,"abstract":"Person Re-Identification problem aims at matching people across a network of non-overlapping cameras. When multiple probe people appear concurrently, human could compare them together to give a more accurate matching. However, existing approaches treat each probe person independently, skipping the concurrent information. In this paper, we propose a re-ranking method which utilize that kind of information to refine ranked lists produced by any person re-identification method to create more precise ranked lists. The experimental results on VIPeR dataset show the improved performance when our method is applied.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129958993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Complex systems simulation online visual analysis and assessment using dynamic aggregation operators","authors":"A. Grignard, A. Drogoul, Jean-Daniel Zucker","doi":"10.1109/SOCPAR.2013.7054144","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054144","url":null,"abstract":"Agent-Based Simulations generate massive loads of data that need to be analyzed and visualized online. This paper proposes an approach that represents and abstracts dynamical properties of agent-based model using visual abstract operator. We present how spatio-temporal dynamic aggregation operators can facilitate online analysis tool of agent-based simulation. Spatial aggregation is used to represent multi level model with hierarchical and density-based clustering. Temporal aggregation is used for agent trajectory analysis and dynamic network analysis. Such an approach is related to data stream mining which is the process of extracting knowledge structures from continuous data records. It goes beyond visualization as the information displayed is the result of mining algorithms that are performed on the stream of data from the ABM simulation.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123365938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Fujino, Y. Mitani, Takaya Hayashi, Y. Fujita, Y. Hamamoto, Makoto Segawa, S. Terai, I. Sakaida
{"title":"A note of liver cirrhosis classification on M-mode ultrasound images by higher-order local auto-correlation features","authors":"K. Fujino, Y. Mitani, Takaya Hayashi, Y. Fujita, Y. Hamamoto, Makoto Segawa, S. Terai, I. Sakaida","doi":"10.1109/SOCPAR.2013.7054099","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054099","url":null,"abstract":"Ultrasound images are widely used for diagnosis of liver cirrhosis. In liver cirrhosis classification using M-mode ultrasound images, Zhou's method has been shown to be effective. However, in Zhou's approach, the liver cirrhosis classification performance depends on the accuracy of the abdominal aorta wall extraction. Therefore, we examine to classify the liver cirrhosis not using the abdominal aorta wall extraction process. In this paper, we propose a liver cirrhosis classification method using higher-order local auto-correlation (HLAC) features. Furthermore, we also propose to use image processing techniques of a thresholding technique and a shading technique to effectively extract the HLAC features. Experimental results show that the proposed method is promising.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127432201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}