کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
472304 | 698704 | 2012 | 11 صفحه PDF | دانلود رایگان |
Focusing on the problem of localized content-based image retrieval, based on probabilistic latent semantic analysis (PLSA) and transductive support vector machine (TSVM), a novel semi-supervised multi-instance learning (SSMIL) algorithm is proposed, where a bag corresponds to an image and an instance corresponds to the low-level visual features of a segmented region. In order to convert an MIL problem into a standard supervised learning problem, first, all the instances in training bags be clustered by KK-Means method, and regards each cluster center as “visual-word” to build a visual vocabulary. Second, according to the distance between “visual-word” and instance, a fuzzy membership function is defined to establish a fuzzy term-document matrix, then use PLSA method to obtain bag’s (image’s) latent topic models, which can convert every bag to a single sample. Finally, in order to use the unlabeled images to improve retrieval accuracy, using semi-supervised TSVM to train classifiers. Experimental results on the COREL data sets show that the proposed method, named PLSA-SSMIL, is robust, and its performance is superior to other key existing MIL algorithms.
Journal: Computers & Mathematics with Applications - Volume 64, Issue 4, August 2012, Pages 500–510