Abstract
Relevance feedback (RF) is an iterative process which improves the performance of content-based image retrieval by modifying the query and similarity metric based on the user’s feedback on the retrieval results. This short-term learning within a single query session is called intra-query learning. However, the interaction history of previous users over all past queries may also be potentially exploited to help improve the retrieval performance for the current query. The long-term learning accumulated over the course of many query sessions is called inter-query learning. We present a novel RF framework that learns one-class support vector machines (1SVM) from retrieval experience to represent the set memberships of users’ high-level concepts and stores them in a “concept database”. The “concept database” provides a mechanism for accumulating inter-query learning obtained from previous queries. By doing a fuzzy classification of a query into the regions of support represented by the 1SVMs, past experience is merged with current intra-query learning. The geometric view of 1SVM allows a straightforward interpretation of the density of past interaction in a local area of the feature space and thus allows the decision of exploiting past information only if enough past exploration of the local area has occurred. The proposed approach is evaluated on real data sets and compared against both traditional intra-query-learning-only RF approaches and other methods that also exploit inter-query learning.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Balabanovic M (1998) Exploring versus exploiting when learning user models for text recommendation. User Model User Adapted Inter 8(1–2):71–102
Burges C (1998) A tutorial on support vector machines for pattern recognition. Data Mining Knowl Discov 2(2):121–167
Chen Y, Zhou X, Huang T (2001) One-class svm for learning in image retrieval. In: Proceedings of the 8th IEEE international conference on image processing, Thessaloniki, pp 34–37
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge
Ciaccia P, Patella M, Zezula P (1997) M-tree: an efficient access method for similarity search in metric spaces. In: Proceedings of the 23rd international conference on very large databases, pp 426–435
Deerwester S, Dumais S, Landauer T, Furnas G, Harshman R (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci 41(6):391–407
Faloutsos C, Flocker M, Niblack W, Petkovic D, Equitz W, Barber R (1993) Efficient and effective querying by image content. Technical Report RJ 9453 (83074), IBM Research Report
Heisterkamp D (2002) Building a latent-semantic index of an image database from patterns of relevance feedback. In: Proceedings of the 16th international conference on pattern recognition, Quebec City, pp 132–135
Hong P, Tian Q, Huang T (2000) Incorporate support vector machines to content-based image retrieval with relevance feedback. In: Proceedings of the 7th IEEE international conference on image processing, Vancouver, pp 750–753
Hoppner F, Klawonn F, Kruse R, Runkler T (1999) Fuzzy cluster analysis. Wiley, New York
Li M, Chen Z, Zhang H (2002) Statistical correlation analysis in image retrieval. Pattern Recognit 35(12):2687–2693
Niblack W, Barber R, Equitz W, Flickner M, Glasman E, Petkovic D, Yanker P, Faloutsos C, Taubin G (1993) The QIBC project: querying images by content using color, texture, and shape. In: Proceedings of storage and retrieval for image and video databases, pp 173–187
Peng J, Banerjee B, Heisterkamp D (2002) Kernel index for relevance feedback retrieval in large image databases. In: Proceedings of the 9th international conference on neural information processing, Singapore
Peng J, Bhanu B, Qing S (1999) Probabilistic feature relevance learning for content-based image retrieval. Comput Vis Image Understanding 75(1/2):150–164
Rui Y, Huang T (1998) Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans Circuits Syst Video Technol 8(5):644–655
Rui Y, Huang T, Mehrotra S (1997) Content-based image retrieval with relevance feedback in mars. In: Proceedings of the 4th IEEE international conference on image processing, Santa Barbara, pp 815–818
Scholkopf B, Williamson R, Smola A, Shawe-Taylor J (1999) SV estimation of a distribution’s support. In: Proceedings of the 13th neural information processing systems, Denver, pp 582–588
Tax D (2001) One-class classification. PhD Thesis, Delft University of Technology
Tong S, Chang E (2001) Support vector machine active learning for image retrieval. In: Proceedings of the 9th ACM international conference on multimedia, Ottawa, pp 107–118
Yin P, Bhanu B, Chang K, Dong A (2002) Improving retrieval performance by long-term relevance information. In: Proceedings of the 16th international conference on pattern recognition, Quebec City, pp 533–536
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gondra, I., Heisterkamp, D.R. & Peng, J. Improving image retrieval performance by inter-query learning with one-class support vector machines. Neural Comput & Applic 13, 130–139 (2004). https://doi.org/10.1007/s00521-004-0415-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-004-0415-2