Technical Program

Paper Detail

Paper:	IMDSP-P4.4
Session:	Image/Video Indexing and Retrieval
Time:	Tuesday, May 16, 16:30 - 18:30
Presentation:	Poster
Topic:	Image and Multidimensional Signal Processing: Image Indexing and Retrieval
Title:	A SHORT-TERM AND LONG-TERM LEARNING APPROACH FOR CONTENT-BASED IMAGE RETRIEVAL
Authors:	Michael Wacht, Slippery Rock University, United States; Juan Shan, Xiaojun Qi, Utah State University, United States
Abstract:	This paper proposes a short-term and long-term learning approach for content-based image retrieval. The proposed system integrates the user’s positive and negative feedback from all iterations to construct a semantic space to remember the user’s intent in terms of the high-level hidden semantic features. The short-term learning further refines the query by updating its associated weight vector using both positive and negative examples together with the long-term-learning-based semantic space. The similarity score is computed as the dot product between the query weight vector and the high-level features of each image stored in the semantic space. Our proposed retrieval approach demonstrates a promising retrieval performance for an image database of 6000 general-purpose images from COREL, as compared with the conventional retrieval systems.