Multitraining support vector machine for image retrieval

Li, Jing, Allinson, Nigel, Tao, Dacheng and Li, Xuelong (2006) Multitraining support vector machine for image retrieval. IEEE Transactions on Image Processing, 15 (11). pp. 3597-3601. ISSN 1057-7149

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Relevance feedback (RF) schemes based on support vector machines (SVMs) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based RF approaches is often poor when the number of labeled feedback samples is small. This is mainly due to 1) the SVM classifier being unstable for small-size training sets because its optimal hyper plane is too sensitive to the training examples; and 2) the kernel method being ineffective because the feature dimension is much greater than the size of the training samples. In this paper, we develop a new machine learning technique, multitraining SVM (MTSVM), which combines the merits of the cotraining technique and a random sampling method in the feature space. Based on the proposed MTSVM algorithm, the above two problems can be mitigated. Experiments are carried out on a large image set of some 20 000 images, and the preliminary results demonstrate that the developed method consistently improves the performance over conventional SVM-based RFs in terms of precision and standard deviation, which are used to evaluate the effectiveness and robustness of a RF algorithm, respectively. © 2006 IEEE.

Keywords:Education computing, Learning algorithms, Learning systems, Random processes, Sampling, Content-based image retrieval, Cotraining technique, Random sampling method, Relevance feedback, Support vector machine, Content based retrieval, algorithm, artificial intelligence, automated pattern recognition, cluster analysis, computer assisted diagnosis, evaluation, image enhancement, information retrieval, letter, methodology, Algorithms, Image Interpretation, Computer-Assisted, Information Storage and Retrieval, Pattern Recognition, Automated
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:8560
Deposited On:12 Apr 2013 10:45

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