Collaborative image retrieval via regularized metric learning

In content-based image retrieval (CBIR), relevant images are identified based on their similarities to query images. Most CBIR algorithms are hindered by the semantic gap between the low-level image features used for computing image similarity and the high-level semantic concepts conveyed in images. One way to reduce the semantic gap is to utilize the log data of users' feedback that has been collected by CBIR systems in history, which is also called "collaborative image retrieval". In this paper, we present a novel metric learning approach, named "regularized metric learning", for collaborative image retrieval, which learns a distance metric by exploring the correlation between low-level image features and the log data of users' relevance judgments. Compared to the previous research, a regularization mechanism is used in our algorithm to effectively prevent overfitting. Meanwhile, we formulate the proposed learning algorithm into a semi-definite programm...

• Type: Journal
• Year: 2006
• Where: MMS
• Authors: Luo Si, Rong Jin, Steven C. H. Hoi, Michael R. Lyu
• Keywords: Image, Image Retrieval, Log Data, MMS 2006, MMS 2007