Abstract
Learning from samples in cases where many high-dimensional vectors but only few samples are available is commonly considered a challenging problem in content-based image retrieval (CBIR). In this paper, we propose an algorithm for metric learning based on spatial distribution of image features. The optimal distance metric is then found by minimizing the divergence between the two distributions. The key idea is to construct a global metric matrix that minimizes the cluster distortions, namely, one that reduces high variances and expands low variances for the data to make a spherical form as good as possible in the high-dimensional data spaces. Experimental results show that our approach is effective in improving the performance of CBIR systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hoi SCH, Liu W, Lyu MR, Ma W-Y (2006) Learning distance metrics with contextual constraints for image retrieval. In: Proceedings of the computer vision and pattern recognition
Shental N, Hertz T, Weinshall D, Pavel M (2002) Adjustment learning and relevant component analysis. In: ECCV 2002, pp 776–792
Davis JV, Kulis B, Jain P, Sra S, Dhillon IS (2007) Information theoretic metric learning. In: Proceedings of the international conference on machine learning, Corvalis, Oregon, pp 209–216
Yang L, Sukthankar R, Hoi SCH (2010) A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval. IEEE Trans Pattern Anal Mach Intell 32(1):30–44
Chen J, Wang R, Shan S, Chen X, Gao X (2006) Isomap based on the image euclidean distance. In: The IEEE 7th international conference on pattern recognition (ICPR2006), pp 1110–1113
Wang Liwei, Zhang Yan, Feng Jufu (2005) On the Euclidean distance of images. IEEE Trans Pattern Anal Mach Intell 27(8):1334–1339
Luo X, Shishibori M, Ren F, Kita K (2007) Incorporate feature space transformation to content-based image retrieval with relevance feedback. Int J Innovative Comput Inf Control (IJICIC) 3(5):1237–1250
Balmachnova E, Florack L, ter Haar Romeny B (2007) Feature vector similarity based on local structure. In: SSVM 2007, pp 386–393
Ishikawa Y, Subramanya R, Faloutsos C (1998) MindReader: querying database through multiple examples. In: Proceedings of the 24th international conference on very large database, pp 218–227
Niblack W, Barber R, Equitz W, Flickner M, Glasman E, Pektovic D, Yanker P, Faloutsos C, Taubin G (1993) The QBIC project: querying images by content using color, texture, and shape. In: Proceedings of SPIE storage and retrieval for image and video databases, pp 173–181
Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of large image data. IEEE Trans PAMI 18(8):837–842
Acknowledgment
This research was partially supported by “the Fundamental Research Funds for the Central Universities (No. 13D11205).”
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Luo, X., Wu, G., Kita, K. (2014). Learning Distance Metrics with Feature Space Performance for Image Retrieval. In: Jia, L., Liu, Z., Qin, Y., Zhao, M., Diao, L. (eds) Proceedings of the 2013 International Conference on Electrical and Information Technologies for Rail Transportation (EITRT2013)-Volume II. Lecture Notes in Electrical Engineering, vol 288. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53751-6_44
Download citation
DOI: https://doi.org/10.1007/978-3-642-53751-6_44
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-53750-9
Online ISBN: 978-3-642-53751-6
eBook Packages: EngineeringEngineering (R0)