NDLI: A practical SVM-based algorithm for ordinal regression in image retrieval

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A practical SVM-based algorithm for ordinal regression in image retrieval

Content Provider	ACM Digital Library
Author	Ma, Songde Lu, Hanqing Wu, Hong
Abstract	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.
Starting Page	612
Ending Page	621
Page Count	10
File Format	PDF
ISBN	1581137222
DOI	10.1145/957013.957144
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2003-11-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Relevance feedback Ordinal regression Support vector machine Cascade linear model
Content Type	Text
Resource Type	Article

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