Search for: semantic-spaces
Article Journal of Computer Science and Technology ; Volume 27, Issue 1 , January , 2012 , Pages 147-162 ; 10009000 (ISSN) ; Lakdashti, A ; Sharif University of Technology
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...
Article 3rd International Conference on Semantics, Knowledge, and Grid, SKG 2007, Xi'an, 29 October 2007 through 31 October 2007 ; 2007 , Pages 322-325 ; 0769530079 (ISBN); 9780769530079 (ISBN) ; Habibi, J ; Rostami, H ; Sharif University of Technology
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...
Beyond bag-of-words: An improved Sparse Topical Coding for learning motion patterns in traffic scenes, Article 9th Iranian Conference on Machine Vision and Image Processing, 18 November 2015 through 19 November 2015 ; Volume 2016-February , 2015 , Pages 1-4 ; 21666776 (ISSN) ; 9781467385398 (ISBN) ; Tabandeh, M ; Gholampour, I ; Sharif University of Technology
IEEE Computer Society
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...