This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Image Retrieval Based on Regions of Interest
July/August 2003 (vol. 15 no. 4)
pp. 1045-1049

Abstract—Query-by-example is the most popular query model in recent content-based image retrieval (CBIR) systems. A typical query image includes relevant objects (e.g., Eiffel Tower), but also irrelevant image areas (including background). The irrelevant areas limit the effectiveness of existing CBIR systems. To overcome this limitation, the system must be able to determine similarity based on relevant regions alone. We call this class of queries region-of-interest (ROI) queries and propose a technique for processing them in a sampling-based matching framework. A new similarity model is presented and an indexing technique for this new environment is proposed. Our experimental results confirm that traditional approaches, such as Local Color Histogram and Correlogram, suffer from the involvement of irrelevant regions. Our method can handle ROI queries and provide significantly better performance. We also assessed the performance of the proposed indexing technique. The results clearly show that our retrieval procedure is effective for large image data sets.

[1] W. Niblack, R. Barber, W. Equitz, M. Flicker, E. Glasman, D. Petkovic, P. Yanker, and C. Faloutsos, The Qbic Project: Query Images by Content Using Color, Texture and Shape SPIE V1908, 1993.
[2] J.R. Smith and S.F. Chang, Visualseek: A Fully Automated Content-Based Image Query System Proc. 1996 ACM Int'l Multimedia Conf., pp. 87-98, 1996.
[3] W.Y. Ma and B.S. Manjunath, Netra: A Toolbox for Navigating Large Image Databases Multimedia System, vol. 7, pp. 184-198, May 1999.
[4] C. Carson, M. Thomas, S. Belongie, and J.M. Hellerstein, J. Malik, Blobworld: A System for Region-Based Image Indexing and Retrieval Proc. Third Int'l Conf. Visual Information Systems, pp. 509-516, 1999.
[5] K.A. Hua, K. Vu, and J.H. Oh, Sammatch: A Flexible and Efficient Sampling-Based Image Retrieval Technique for Large Image Databases Proc. 1999 ACM Int'l Multimedia Conf., pp. 225-234, Oct. 1999.
[6] M. Hiyahara and Y. Yoshida, Mathematical Transform of (r, g, b) Color Data to Munsell (h, v, c) Color Data SPIE Visual Comm. and Image Processing, pp. 650-657, 1988.
[7] N. Beckman, H.P. Kriegel, R. Schneider, and B. Seeger, The${\rm{r}}{\ast}{\hbox{-}}{\rm{tree}}$: An Efficient and Robust Access Method for Points and Rectangles ACM SIGMOD, pp. 322-331, May 1990.
[8] K. Vu, K.A. Hua, and D.A. Tran, An Efficient Core-Area Detection Algorithm for Fast Noise-Free Image Query Processing Proc. 16th ACM-SIGAPP Ann. Symp. Applied Computing, pp. 258-263, Mar. 2001.
[9] J. Malki, N. Boujemaa, C. Nastar, and A. Winter, Region Queries without Segmentation for Image Retrieval by Content Proc. Third Int'l Conf. Visual Information and Information Systems, pp. 115-122, 1999.
[10] J. Huang, S.R. Kumar, M. Mitra, W. Zhu, and R. Zabih, Image Indexing Using Color Correlograms Proc. Computer Vision and Pattern Recognition, pp. 762-768, 1997.
[11] A. Natsev, R. Rastogi, and K. Shim, Walrus: A Similarity Retrieval Algorithm for Image Databases Proc. 1999 ACM SIGMOD Int'l Conf. Management of Data, pp. 395-406, 1999.
[12] N. Nes and M.C. d'Ornellas, Color Image Texture Indexing Proc. Third Int'l Conf. Visual Information Systems, pp. 467-474, 1999.

Index Terms:
Image processing, image indexing and retrieval, regions of interest, arbitrary-shaped queries.
Citation:
Khanh Vu, Kien A. Hua, Wallapak Tavanapong, "Image Retrieval Based on Regions of Interest," IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 4, pp. 1045-1049, July-Aug. 2003, doi:10.1109/TKDE.2003.1209021
Usage of this product signifies your acceptance of the Terms of Use.