Locating regions of interest in CBIR with multi-instance learning techniques

Authors:
Zhi-Hua Zhou;Xiao-Bing Xue;Yuan Jiang
Affiliations:
National Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;National Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;National Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

In content-based image retrieval (CBIR), the user usually poses several labelled images and then the system attempts to retrieve all the images relevant to the target concept defined by these labelled images. It may be helpful if the system can return relevant images where the regions of interest (ROI) are explicitly located. In this paper, this task is accomplished with the help of multi-instance learning techniques. In detail, this paper proposes the CkNN-ROI algorithm, which regards each image as a bag comprising many instances and picks from positive bag the instance that has great chance to meet the target concept to help locate ROI. Experiments show that the proposed algorithm can efficiently locate ROI in CBIR process.