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.
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Zhou, ZH., Xue, XB., Jiang, Y. (2005). Locating Regions of Interest in CBIR with Multi-instance Learning Techniques. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_12
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DOI: https://doi.org/10.1007/11589990_12
Publisher Name: Springer, Berlin, Heidelberg
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