A novel log-based relevance feedback technique in content-based image retrieval

Relevance feedback has been proposed as an important technique to boost the retrieval performance in content-based image retrieval (CBIR). However, since there exists a semantic gap between low-level features and high-level semantic concepts in CBIR, typical relevance feedback techniques need to perform a lot of rounds of feedback for achieving satisfactory results. These procedures are time-consuming and may make the users bored in the retrieval tasks. For a long-term study purpose in CBIR, we notice that the users’ feedback logs can be available and employed for helping the retrieval tasks in CBIR systems. In this paper, we propose a novel scheme to study the log-based relevance feedback (LRF) technique for improving retrieval performance and reducing the semantic gap in CBIR. In order to effectively incorporate the users’ feedback logs, we propose a modified support vector machine (SVM) technique called soft label support vector machine (SLSVM) to construct the LRF algorithm ...

• Type: Conference
• Year: 2004
• Where: MM
• Authors: Chu-Hong Hoi, Michael R. Lyu
• Keywords: Image Retrieval, MM 2004, Relevance Feedback, Retrieval Performance