Abstract
Most learning algorithms for image retrieval are based on dichotomy relevance judgement (relevance and non-relevance), though this measurement of relevance is too coarse. To better identify the user needs and preference, a good retrieval system should be able to handle multilevel relevance judgement. In this paper, we focus on relevance feedback with multilevel relevance judgment. We consider relevance feedback as an ordinal regression problem, and discuss its properties and loss function. Since traditional performance measures such as precision and recall are based on dichotomy relevance judgment, we adopt a performance measure that is based on the preference of one image to another one. Furthermore, we develop a new relevance feedback scheme based on a support vector learning algorithm for ordinal regression. Our solution is tested on real image database, and promising results are achieved.
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Wu, H., Lu, H., Ma, S. (2003). Multilevel Relevance Judgment, Loss Function, and Performance Measure in Image Retrieval. In: Bakker, E.M., Lew, M.S., Huang, T.S., Sebe, N., Zhou, X.S. (eds) Image and Video Retrieval. CIVR 2003. Lecture Notes in Computer Science, vol 2728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45113-7_11
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DOI: https://doi.org/10.1007/3-540-45113-7_11
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