Sharif Digital Repository

In this paper, we propose a mapping from low level feature space to the semantic space drawn by the users through relevance feedback to enhance the performance of current content based image retrieval (CBIR) systems. The proposed approach makes a rule base for its inference and configures it using the feedbacks gathered from users during the life cycle of the system. Each rule makes a hypercube (HC) in the feature space corresponding to a semantic concept in the semantic space. Both short and long term strategies are taken to improve the accuracy of the system in response to each feedback of the user and gradually bridge the semantic gap. A scoring paradigm is designed to determine the... 

Peer-to-peer networks are beginning to form the infrastructure of future applications. The use of semantic information to organize nodes in a P2P network is a prosperous approach. In this paper we present a novel method, called Semantic Partition Tree, which organizes nodes in a semantic space, using an upper ontology. In this approach each node can find a target in a time proportioned to the depth of the upper ontology, which is significantly less than the number of nodes in the network. Also we simulated our method with several configurations and measured its performance. The results of the simulation show that our scheme is highly scalable and can be used by real P2P networks. © 2007 IEEE... 

Analyzing motion patterns in traffic videos can directly generate some high-level descriptions of the video content which can be further employed in rule mining and abnormal event detection. The most recent and successful unsupervised approaches for complex traffic scene analysis are based on topic models. However, most existing topic models share some key characteristics which could limit their utility. In this paper, based on extracted optical flow features from video clips, we employ Sparse Topical Coding (STC) framework to automatically discover typical motion patterns in traffic scenes. For this purpose, we improve the STC to overcome one of the drawbacks of topic models with the aim of...