Abstract
We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search.
Similar content being viewed by others
Abbreviations
- CBIR:
-
Content-based Image Retrieval
- RF:
-
Relevance Feedback
- OM:
-
Ostensive Model
References
Black JA Jr, Fahmy G, Panchanathan S (2002) A method for evaluating the performance of content-based image retrieval systems based on subjectively determined similarity between images. In: Proc. Int. Conf. on Image and Video Retrieval, LNCS 2383, pp 356–366
Campbell I (2000) Interactive evaluation of the Ostensive Model, using a new test-collection of images with multiple relevance assessments. Inf Retr 2(1):89–114
Campbell I (2000) The Ostensive Model of developing information needs. Ph.D. thesis, University of Glasgow
Campbell I, van Rijsbergen CJ (1996) The Ostensive Model of developing information needs. In: Proc. Int. Conf. on Conceptions of Library and Information Science, pp 251–268
Chalmers M, Rodden K, Brodbeck D (1998) The order of things: activity-centred information access. Comput Netw ISDN Syst 30(1–7):359–367
Cox IJ, Miller ML, Minka TP, Papathomas TV, Yianilos PN (2000) The Bayesian image retrieval system, PicHunter: Theory, implementation and psychophysical experiments. IEEE Trans Image Process 9:20–37
Dunlop M (2000) Reflections on Mira: Interactive evaluation in information retrieval. J Am Soc Inf Sci 51(14):1269–1274
Garber SR, Grunes MB (1992) The art of search: a study of art directors. In: Proc. ACM Int. Conf. on Human Factors in Computing Systems (CHI'92), 1992, pp 157–163
Hu M-K (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory IT-8:179–187
Ingwersen P (1992) Information retrieval interaction. Taylor Graham, London
Ishikawa Y, Subramanya R, Faloutsos C (1998) MindReader: Querying databases through multiple examples. In: Proc. 24th Int. Conf. on Very Large Data Bases, pp 218–227
Jose JM (1998) An integrated approach for multimedia information retrieval. Ph.D. thesis, The Robert Gordon University, Aberdeen
Jose JM, Furner J, Harper DJ (1998) Spatial querying for image retrieval: a user-oriented evaluation. In: Proc. Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp 232–240
Jose JM, Harper DJ (1997) A retrieval mechanism for semi-structured photographic collections. In: Proc. of the Int. Conf. on Database and Expert Systems Applications, 1997, pp 276–292
Markkula M, Sormunen E (2000) End-user searching challenges indexing practices in the digital newspaper photo archive. Inf Retr 1(4):259–285
McDonald S, Lai T-S, Tait J (2001) Evaluating a content based image retrieval system. In: Proc. Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp 232–240
Peng J, Bhanu B, Qing S (1999) Probabilistic feature relevance learning for content-based image retrieval. Comput Vis Image Underst 75(1/2):150–164
Porkaew K, Chakrabarti K, Mehrotra S (1999) Query refinement for multimedia similarity retrieval in MARS. In: Proc. ACM Int. Conf. on Multimedia, pp 235–238
Rocchio JJ (1971) Relevance feedback in information retrieval. In: Salton G (ed) The SMART retrieval system: experiments in automatic document processing. Prentice-Hall, Englewood Cliffs, New Jersey, pp 313–323
Rui Y, Huang TS (2000) Optimizing learning in image retrieval. In: IEEE Proc. Conf. on Computer Vision and Pattern Recognition, pp 236–245
Salton G, Buckley C (1990) Improving retrieval performance by relevance feedback. J Am Soc Inf Sci 41(4):288–297
Salton G, McGill MJ (1983) Introduction to modern information retrieval. McGraw-Hill, Tokyo
Santini S, Gupta A, Jain R (2001) Emergent semantics through interaction in image databases. IEEE Trans Knowl Data Eng 13(3):337–351
Sonka M, Hlavac V, Boyle R (1998) Image processing, analysis, and machine vision, 2nd edition. Brooks and Cole
Squire DM, Pun T (1998) Assessing agreement between human and machine clustering of image databases. Pattern Recogn 31(12):1905–1919
Stricker M, Orengo M (1995) Similarity of color images. In: Proc. SPIE: Storage and Retrieval for Image and Video Databases, pp 381–392
Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis (Kluwer) 7(1):11–32
ter Hofstede AHM, Proper HA, van der Weide TP (1996) Query formulation as an information retrieval problem. Comput J 39(4):255–274
Tieu K, Viola P (2000) Boosting image retrieval. In: IEEE Proc. Conf. on Computer Vision and Pattern Recognition, pp 228–235
Tong S, Chang E (2001) Support vector machine active learning for image retrieval. In: Proc. ACM Int. Conf. on Multimedia, pp 107–118
Urban J, Jose JM, van Rijsbergen CJ (2003) An adaptive approach towards content-based image retrieval. In: Proc. Int. Workshop on Content-Based Multimedia Indexing (CBMI'03), pp 119–126
White RW, Jose JM, Ruthven I (2003) An approach for implicitly detecting information needs. In: Proc. Int. Conf. on Information and Knowledge Management, pp 504–507
White RW, Ruthven I, Jose JM (2002) Finding relevant documents using top ranking sentences: an evaluation of two alternative schemes. In: Proc. Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp 446–446
Wood MEJ, Thomas BT, Campbell NW (1998) Iterative refinement by relevance feedback in content-based digital image retrieval. In: Proc. ACM Int. Conf. on Multimedia, pp 13–20
Zhou XS, Huang T (2003) Relevance feedback in image retrieval: a comprehensive review. ACM Multimedia Systems Journal 8(6):536–544
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Urban, J., Jose, J.M. & van Rijsbergen, C.J. An adaptive technique for content-based image retrieval. Multimed Tools Appl 31, 1–28 (2006). https://doi.org/10.1007/s11042-006-0035-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-006-0035-1