Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Comparing discriminating transformations and SVM for learning during multimedia retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Exploring the Nature and Variants of Relevance Feedback
CBAIVL '01 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01)
Adaptable similarity search using non-relevant information
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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Inherent subjectivity in user's perception of an image has motivated the use of relevance feedback (RF) in the image desigined output's retrieval process. RF techniques interactively determine the user's query concept, given the user's relevance judgments on a set of images. In this paper we propose a robust technique that utilizes non-relevant images to efficiently discover the relevant search region. A similarity metric, estimated using the relevant images is then used to rank and retrieve database images in the relevant region. The partitioning of the feature space is achieved by using a piecewise linear decision surface that separates the relevant and non-relevant images. Each of the hyperplanes constituting the decision surface is normal to the minimum distance vector from a non-relevant point to the convex hull of relevant points. Experimental results demonstrate significant improvement in retrieval performance for the small feedback size scenario over two well established RF algorithms.