انجمن کامپیوتر ایران

Video processing techniques based on pattern recognition methods and machine vision is one of the interesting research fields which attract many researchers. In this paper, we proposed a novel method for video summarization using genetic algorithm based on information theory. Our method relies on the mutual information for video summarization. The information theory measure provides us with better results because it extracts the inter-frame information. We present that it is a suitable factor for summarizing video, which maintains its integrity.

In this paper we propose several methods for partitioning, the process of grouping members of population to different memeplexes, in a shuffled frog leaping algorithm. These proposed methods divide the population in terms of the value of cost function or the geometric position of members or quite random partitioning. The proposed methods are evaluated on several low and high dimensional benchmark functions. The obtained results on low dimensional functions demonstrate that geometric partitioning methods have the best success rate and the fastest performance. Also on high dimensional functions, however using of the geometric partitioning methods for the partitioning stage of the SFL algorithm lead to a better success rate but these methods are more time consuming than other partitioning methods.

In this paper, we proposed a new feature subset selection approach. In proposed approach first, the entire dataset are classified and the best number of clusters over it are found according to silhouette value. Then according to this value, each feature is alone classified with the same cluster number and accordingly the proposed entropy fuzzy measure is found for them. We examine our method on some traditional datasets. The results show a good performance of proposed method.

Uncertainty reasoning has been identified as an important and challenging issue in the database research. Many logic frameworks have been proposed to represent and reason about uncertainty in deductive databases. On the basis of the way in which uncertainties are associated with the facts and rules in programs, the approaches of these frameworks have been classified into “annotation based (AB)” and “implication based (IB).” When extending both frameworks with certainty constraints, they become equivalent in terms of expressive power. In this paper, we propose a uniform environment to evaluate and experiment with logic programs in AB and IB frameworks at the same time. We also extend the existing query processing to handle certainty constraints and we carry out experiments to evaluate its performance. Our experiments and results indicate that the proposed techniques yield tools that are capable to reason with uncertainty.