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LSA based multi-instance learning algorithm for image retrieval
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DOI：10.1016/j.sigpro.2011.03.004.png)
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Abstract
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Focusing on the problem of natural image retrieval, based on latent semantic analysis (LSA) and support vector machine (SVM), a novel multi-instance learning (MIL) algorithm is proposed, where a bag corresponds to an image and an instance corresponds to the low-level visual features of a segmented region. Firstly, in order to transform every bag into a single sample, a collection of visual-word is generated by k-means clustering method to construct a projection space, then a nonlinear mapping is defined using these visual-word to embed each bag as a point in the projection space, thereby obtaining every bag's projection feature. Secondly, the matrix consisted of all the projection features of training bags is regarded as a term-document matrix, and LSA method is used to obtain the latent semantic feature of each bag. As a result, the MIL problem is converted into a standard single instance learning (SIL) problem that can be solved directly by SVM method. Experimental results on the COREL data sets show that the proposed method, named LSASVM-MIL, is robust, and its performance is superior to other key existing MIL algorithms. (C) 2011 Elsevier B.V. All rights reserved.
Keywords:
Multi-instance learning (MIL)
Image retrieval
Latent semantic analysis (LSA)
Support vector machine (SVM)
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