Abstract
This paper presents a cluster-based relevance feedback method, which combines two popular techniques of relevance feedback: query point movement and query expansion. Inspired from text retrieval, these two techniques are giving good results for image retrieval. But query point movement is limited by a constraint of unimodality in taking into account the user feedbacks. Query expansion gives better results than query point movement, but it cannot take into account irrelevant images from the user feedbacks. We combine the two techniques to profit from their advantages and to cope with their limitations. From a single point initial query, query expansion provides a multiple point query, which is then enhanced using query point movement. To learn the multiple point queries, the irrelevant feedback images are classified into query points which are clustered from relevant images using the query expansion technique. The experiments show that our method gives better results in comparison with the two techniques of relevance feedback taken individually.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Apostol N, Milind N, Jelena T (2005) Learning the semantics of multimedia queries and concepts from a small number of examples. In: MULTIMEDIA ’05: Proceedings of the 13th annual ACM international conference on Multimedia, ACM, New York, NY, USA, pp 598–607
Danzhou L, Hua A, Vu K, Yu N (2009) Fast query point movement techniques for large cbir systems. IEEE Trans Knowl and Data Eng 21(5):729–743
Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2007) The PASCAL Visual Object Classes Challenge 2011 (VOC2011) Results. http://www.pascal-network.org/challenges/VOC/voc2011/workshop/index.html
Faria FF, Veloso A, Almeida HM, Valle E, Torres RdS, Gonçalves MA, Meira W Jr (2010) Learning to rank for content-based image retrieval. In: Proceedings of the international conference on Multimedia information retrieval, MIR ’10, ACM, New York, NY, USA, pp 285–294
Frigui H, Krishnapuram R (1997) Clustering by competitive agglomeration. Pattern Recognition 30(7):1109 – 1119
Griffin G, Holub A, Perona P (2007) Caltech-256 object category dataset. Tech. Rep. 7694, California Institute of Technology, http://authors.library.caltech.edu/7694
Gustavo C, Chan B, Moreno J, Vasconcelos N (2007) Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans Pattern Anal Mach Intell 29(3):394–410
Huiskes J, Lew S (2008) Performance evaluation of relevance feedback methods. In: CIVR ’08: Proceedings of the 2008 international conference on Content-based image and video retrieval, ACM, New York, NY, USA, pp 239–248
Karthik PS, Jawahar CV (2006) Analysis of relevance feedback in content based image retrieval. In: Ninth international conference on control automation robotics and vision, 2006, pp 1–6
Kim D, Chung C, Barnard K (2005) Relevance feedback using adaptive clustering for image similarity retrieval. J Syst Softw 78(1):9–23
Kothari R, Pitts D (1999) On finding the number of clusters. Pattern Recogn Lett 20(4):405–416
Natsev A, Smith J (2003) Active selection for multi-example querying by content. In: ICME ’03: proceedings of the 2003 international conference on multimedia and expo, IEEE Computer Society, Washington, DC, USA, pp 445–448
Nguyen NV, Ogier JM, Tabbone S, Boucher A (2009) Text retrieval relevance feedback techniques for bag of words model in cbir. In: International conference on machine learning and pattern recognition (ICMLPR), Paris, France, pp 541–546
Ortega M, Mehrotra S (2004) Relevance feedback techniques in the mars image retrieval system. Multimed Syst 9:535–547
Ritendra D, Dhiraj J, Jia L, James ZW (2008) Image retrieval: Ideas, influences, and trends of the new age. ACM Comput Surv 40(2):1–60
Salton G (ed) (1971) The SMART retrieval system—experiments in automatic document processing. Prentice Hall, Englewood, Cliffs
Sivic J, Zisserman A (2008) Efficient visual search for objects in videos. Proc IEEE 96(4):548–566
Tahaghoghi M, Thom A, Williams E (2002) Multiple example queries in content-based image retrieval. In: SPIRE 2002: proceedings of the ninth international symposium on string processing and information retrieval, Springer-Verlag, London, pp 227–240
Tao D, Tang X, Li X, Wu X (2006) Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans Pattern Anal Mach Intell 28(7):1088–1099
Thijs W, de P Vries Arjen (2004) Multimedia retrieval using multiple examples. In: Image and video retrieval, lecture notes in computer science, vol 3115, Springer, Berlin, pp 2048–2049
Xiangyu J, James CF (2003) Improving image retrieval effectiveness via multiple queries. In: MMDB ’03: Proceedings of the 1st ACM international workshop on Multimedia databases, ACM, New York, NY, USA, pp 86–93
Xuanhui W, Hui F, ChengXiang Z (2008) A study of methods for negative relevance feedback. In: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, ACM, New York, NY, USA, SIGIR ’08, pp 219–226
Yimin W, Aidong Z (2004) Interactive pattern analysis for relevance feedback in multimedia information retrieval. Multimedia Syst 10:41–55
Yoshiharu I, Ravishankar S, Christos F (1998) Mindreader: Querying databases through multiple examples. In: VLDB ’98: Proceedings of the 24rd International Conference on Very Large Data Bases, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 218–227
Zhou S, Huang S (2003) Relevance feedback in image retrieval: a comprehensive review. Multimedia Syst 8(6):536–544
Acknowledgments
This project is supported in part by the ICT-Asia IDEA project from the French Ministry of Foreign Affairs (MAE), the DRI INRIA and DRI CNRS.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nguyen, NV., Boucher, A., Ogier, JM. et al. Cluster-based relevance feedback for CBIR: a combination of query point movement and query expansion. J Ambient Intell Human Comput 3, 281–292 (2012). https://doi.org/10.1007/s12652-012-0141-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-012-0141-z