Multitraining Support Vector Machine for Image Retrieval

• Published in: IEEE transactions on image processing Vol. 15; no. 11; pp. 3597 - 3601
• Main Authors: Li, Jing, Allinson, N., Tao, Dacheng, Li, Xuelong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2006; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Cluster Analysis; Content based retrieval; Content-based image retrieval (CBIR); Feedback; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image retrieval; Information Storage and Retrieval - methods; Kernel; Machine learning; Machine learning algorithms; multitraining SVM (MTSVM); Pattern Recognition, Automated - methods; Radio frequencies; Radio frequency; relevance feedback (RF); Retrieval; Robustness; Sampling methods; Standard deviation; Studies; support vector machine (SVM); Support vector machine classification; Support vector machines; Training
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2006.881938

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