Multi-Instance Learning for Image Retrieval with Relevance Feedback

Abstract:

In this paper, we propose a novel method for image retrieval based on multi-instance learning with relevance feedback. The process of this method mainly includes the following three steps: First, it segments each image into a number of regions, treats images and regions as bags and instances respectively. Second, it constructs an objective function of multi-instance learning with the query images, which is used to rank the images from a large digital repository according to the distance values between the nearest region vector of each image and the maximum of the objective function. Third, based on the users relevance feedback, several rounds may be needed to refine the output images and their ranks. Finally, a satisfying set of images will be returned to users. Experimental results on COREL image data sets have demonstrated the effectiveness of the proposed approach.

• Periodical: Applied Mechanics and Materials (Volumes 427-429)
• Pages: 1606-1609
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.427-429.1606
• Online since: September 2013
• Authors: Tao Chen, Hui Fang Deng
• Keywords: Image Retrieval, Machine Learning (ML), Multi-Instance Learning, Relevance Feedback

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