Abstract
Content-based image retrieval is an important research topic in computer vision. We present a new method that combines region of interest (ROI) detection and relevance feedback. The ROI based approach is more accurate in describing the image content than using global features, and the relevance feedback makes the system to be adaptive to subjective human perception. The feedback information is utilized to discover the subjective ROI perception of a particular user, and it is further employed to recompute the features associated with ROIs with the updated personalized ROI preference. A fast computation technique is proposed to avoid repeating the ROI detection for images in the database. It directly estimates the features of the ROIs, which makes the query process fast and efficient. For illustration of the overall approach, we use the color saliency and wavelet feature saliency to determine the ROIs. Normalized projections are selected to represent the shape features associated with the ROIs. Experimental results show that the proposed system has better performance than the global features based approaches and region based techniques without feedback.
Similar content being viewed by others
References
S. Ardizzoni, I. Bartolini, and M. Patella, “Windsurf: Region-based image retrieval using wavelets,” in Proceedings of the 1st Workshop on Similarity Search, 1999.
Caltech, 2003. http://www.vision.caltech.edu/html-files/archive.html.
C. Carson, S. Belongie, H. Greenspan, and J. Malik, “Blobworld: Image segmentation using expectation-maximization and its application to image querying,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 8, pp. 1026–1038, 2002.
M. Celenk, Q. Zhou, V. Vetness, and R. Godavari, “Adaptive shape transform for color image querying,” in Proc. {SPIE Vol. 5014, Image Processing: Algorithms and Systems II}, 2003, pp. 86–98.
T. Chang and C.J. Kuo, “Texture analysis and classification with tree-structured wavelet transform,” IEEE Transactions on Image Processing, Vol. 2, No. 4, pp. 429–440, 1993.
C. Faloutsos, W. Equitz, M. Flickner, W. Niblack, D. Petkovic, and R. Barber, “Efficient and effective querying by image content,” Journal of Intelligent Information Systems, Vol. 3, Nos. 3/4, pp. 231–262, 1994.
B. Furht, S.W. Smoliar, and H.J. Zhang, Video and Image Processing in Multimedia Systems, Kluwer Academic Publishers, 1995.
S. Geman and D. Geman, “Stochastic relaxation, gibbs distribution, and the bayesian restoration of images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 6, pp. 721–741, 1984.
V.D. Gesu, C. Valenti, and L. Strinati, “Local operators to detect region of interest,” Pattern Recognition Letters, Vol. 18, pp. 1077–1081, 1997.
H.M. Haralick and L.G. Shapiro, Computer and Robot Vision, Vol. I. Addison-Wesley, 1992.
B.K.P. Horn and B.G. Schunch, “Determining optical flow,” Artificial Intelligence, Vol. 17, pp. 185–203, 1981.
F. Jing, M.L.H.-J. Zhang, and B. Zhang, “Region-based relevance feedback in image retrieval,” in Proceedings of {IEEE International Symposium on Circuits and Systems}, 2002.
H.F. Lau and M.D. Levine, “Finding a small number of region in an image using low-level features,” Pattern Recognition, Vol. 35, pp. 2323–2339, 2002.
C. Lee, W.Y. Ma, and H.J. Zhang, “Information embedding based on user’s relevance feedback for image retrieval,” Technical report, HP Labs, 1998.
J.S. Lim, Two-Dimensional Signal and Image Processing, Prentice Hall, 1990.
Y. Lu, C. Hu, X. Zhu, H. Zhang, and Q. Yang, “A unified framework for semantics and feature based relevance feedback in image retrieval systems,” in {ACM Multimedia}, 2000, pp. 31–37.
W.Y. Ma and B.S. Manjunath, “Netra: A toolbox for navigating large image databases,” Multimedia Systems, Vol. 7, No. 3, pp. 184–198, 1999.
D.J. Marchette, J.L. Solka, R. Guidry, and J. Green, “The advanced distributed region of interest tool,” Pattern Recognition, Vol. 31, No. 12, pp. 2103–2118, 1998.
B. Moghaddam, H. Biermann, and D. Margaritis, “Regions-of-interest and spatial layout for content-based image retrieval,” International Journal of Multimedia Tools and Applications, Vol. 14, No. 2, pp. 201–210, 2001.
M. Nakazato and T.S. Huang, “Extending image retrieval with group-oriented interface,” in Proceedings of {IEEE International Conference on Multimedia and Expo (ICME)}, Vol. 1, 2002, pp. 201–204.
C.C.A.V. Ogle, “Storage and retrieval of feature data for a very large online image collection,” IEEE Computer Society Bulletin of the Technical Committee on Data Engineering, Vol. 9, No. 3, pp. 19–27, 1996.
OSU, 2003. http://sampl.eng.ohio-state.edu/sampl/database.htm.
E.J. Pauwels and G. Frederix, “Finding salient regions in images,” Computer Vision and Image Understanding, Vol. 75, Nos. 1/2, pp. 73–85, 1999.
A. Pentland, R. Picard, and S. Sclaroff, Photobook: Content-Based Manipulation of Image Databases, Chapt. 2, Kluwer Academic Publishers, 1996, pp. 43–80.
Y. Rui and T.S. Huang, “A novel relevance feedback technique in image retrieval,” in Proceedings {ACM Multimedia’99 (Part 2)}, 1999, pp. 67–70.
Y. Rui, T.S. Huang, M. Ortega, and S. Mehrotra, “Relevance feedback: A power tool for interactive content-based image retrieval,” IEEE Transactions on Circuits and Video Technology, Vol. 8, No. 5, pp. 644–655, 1998.
P. Salembier and F. Marques, “Region-based representations of image and video: Segmentation tools for multimedia services,” IEEE Transactions on Circuits and Video Technology, Vol. 9, No. 8, pp. 1147–1169, 1999.
Y. Shao and M. Celenk, “Higher-Order Spectra (HOS) invariants for shape recognition,” Pattern Recognition, Vol. 34, pp. 2097–2113, 2001.
V. Sridhar, M.A. Nascimento, and X. Li, “Region-based image retrieval using multiple features,” in Proceeding of the 2002 Visual Information System Conference, 2002, pp. 61–75.
Q. Tian, Y. Wu, and T.S. Huang, “Combine user defined region-of-interest and spatial layout for image retrieval,” in Proceedings If {IEEE International Conference on Image Processing}, 2000, pp. 746–749.
R.C. Veltkamp and M. Tanase, “A survey of content-based image retrieval systems,” in Content-Based Image and Video Retrieval, O. Marques and B. Furht (Eds.), Kluwer Academic Publishers, 2002, pp. 47–101.
K. Vu, K. Hua, and W. Tavanapong, “Image retrieval based on regions of interest,” IEEE Transactions on Knowledge and Data Engineering, pp. 1045–1049, 2003.
J.Z. Wang, J. Li, and G. Wiederhold, “SIMPLICITY: Semantics-sensitive integrated matching for picture libraries,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, pp. 947–963, 2001.
X.S. Zhou and T.S. Huang, “Comparing discriminating transformations and SVM for learning during multimedia retrieval,” in ACM Multimedia, 2001, pp. 137–146.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhou, Q., Ma, L., Celenk, M. et al. Content-Based Image Retrieval Based on ROI Detection and Relevance Feedback. Multimed Tools Appl 27, 251–281 (2005). https://doi.org/10.1007/s11042-005-2577-z
Issue Date:
DOI: https://doi.org/10.1007/s11042-005-2577-z