26 February 2010 Incorporating multiple SVMs for active feedback in image retrieval using unlabeled data
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Proceedings Volume 7546, Second International Conference on Digital Image Processing; 75462N (2010) https://doi.org/10.1117/12.853383
Event: Second International Conference on Digital Image Processing, 2010, Singapore, Singapore
Abstract
Active learning with support vector machine(SVM) selects most informative unlabeled images for user labeling, however small training samples affect its performance. To improve active learning and use more unlabeled data, we propose a new algorithm training three SVMs separately on the color, texture and shape features of labeled images with three different kernel functions according to the features' distinct statistical properties. Different algorithms are used in the selection of disagreement and agreement samples from unlabeled data and also in the calculation of their confidence degrees. The lowest confident disagreement samples are returned to user to label and added to the training data set with the highest confident agreement samples. Experimental results verify the high effectiveness of our method in image retrieval.
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Zongmin Li, Zongmin Li, Yang Liu, Yang Liu, Hua Li, Hua Li, } "Incorporating multiple SVMs for active feedback in image retrieval using unlabeled data", Proc. SPIE 7546, Second International Conference on Digital Image Processing, 75462N (26 February 2010); doi: 10.1117/12.853383; https://doi.org/10.1117/12.853383
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