Incorporate Support Vector Machines to Content-Based Image Retrieval with Relevant Feedback

By using relevance feedback [6], Content-Based Image Retrieval (CBIR) allows the user to retrieve images interactively. The user can select the most relevant images and provide a weight of preference for each relevant image. The high level concept borne by the user and perception subjectivity of the user can be captured by the system to some degree. This paper proposes an approach to utilize both positive and negative feedbacks for image retrieval. Support Vector Machines (SVM) is applied to classifying the positive and negative images. The SVM learning results are used to update the preference weights for the relevant images. This approach releases the user from manually providing preference weight for each positive example. Experimental results show that the proposed approach has improvement over the previous approach [5] that uses positive examples only.

• Type: Conference
• Year: 2000
• Where: ICIP
• Authors: Pengyu Hong, Qi Tian, Thomas S. Huang
• Keywords: Content-based Image Retrieval, ICIP 2000, Image Processing, Preference Weight, Relevant Images