Linear structure in information retrieval
SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Measuring retrieval effectiveness based on user preference of documents
Journal of the American Society for Information Science
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Information Retrieval
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Relevance Feedback and Category Search in Image Databases
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
A practical SVM-based algorithm for ordinal regression in image retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
The state of the art in image and video retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Multilevel relevance feedback for 3D shape retrieval
EG 3DOR'09 Proceedings of the 2nd Eurographics conference on 3D Object Retrieval
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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.