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Most current learning algorithms for image retrieval are based on dichotomy relevance judgement (relevant and non-relevant), 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, where the relevance feedback is considered as an ordinal regression problem. Herbrich has proposed a support vector learning algorithm for ordinal regression based on the Linear Utility Model. His algorithm is intrinsically to train a SVM on a new derived training set, whose size increases rapidly when the original training set gets bigger. This property limits its applicability in relevance feedback, due to real-time requirement of the interactive process. By thoroughly analyzing Herbrich's algorithm, we first propose a new model for ordinal regression, called Cascade Linear Utility Model, then a practical SVM-based algorithm for image retrieval upon it. Our new algorithm is tested on a real-world image database, and compared with other three algorithms capable to handle multilevel relevance judgment. The experimental results show that the retrieval performance of our algorithm is comparable with that of Herbrich's algorithm but with only a fraction of its computational time, and apparently outperform the other methods.