An Evaluation Strategy for Visual Key Image Retrieval on Mobile Devices
Kazushi Okamoto*1, Kazuhiko Kawamoto*1,*2, Fangyan Dong*3,
Shinichi Yoshida*4, and Kaoru Hirota*3
*1Academic Link Center, Chiba University
*2Institute of Media and Information Technology, Chiba University, 1-33 Yayoicho, Inage-ku, Chiba 263-8522, Japan
*3Dept. of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
*4School of Information, Kochi University of Technology, 185 Tosayamada-cho-Miyanokuchi, Kochi 782-8502, Japan
-  R. da S. Torres et al., “A genetic programming framework for content-based image retrieval,” Pattern Recognition, Vol.42, No.2, pp. 283-292, 2009.
-  J. Laaksonen, M. Koskela, S. Laakso, and E. Oja, “PicSOM content-based image retrieval with self-organizing maps,” Pattern Recognition Letters, Vol.21, Issue 13-14, pp. 1199-1207, 2000.
-  J. Li and J. Z. Wang, “Real-time computerized annotation of pictures,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.30, Issue 6, pp. 985-1002, 2008.
-  Z. Stejić, Y. Takama, and K. Hirota, “Relevance feedback-based image retrieval interface incorporating region and feature saliency patterns as visualizable image similarity criteria,” IEEE Trans. on Industrial Electronics, Vol.50, Issue 5, pp. 839-852, 2003.
-  J. Z. Wang, J. Li, and G. Wiederhold, “SIMPLIcity: Semanticssensitive integrated matching for picture libraries,” IEEE Trans. on Pattern Analysis andMachine Intelligence, Vol.23, Issue 9, pp. 947-963, 2001.
-  J. Fauqueur and N. Boujemaa, “Mental image search by boolean composition of region categories,” Multimedia Tools and Applications, Vol.31, No.1, pp. 95-117, 2006.
-  M. Serata, Y. Hatakeyama, and K. Hirota, “Designing Image Retrieval System with the Concept of Visual Keys,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.10, No.2, pp. 136- 144, 2006.
-  K. Okamoto, F. Dong, S. Yoshida, and K. Hirota, “DCT domain features based image index (in Japanese),” J. of Japan Society for Fuzzy Theory and Intelligent Informatics, Vol.21, No.6, pp. 1092-1102, 2009.
-  G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray, “Visual categorization with bags of keypoints,” In Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1-22, 2004.
-  G. Ding, J. Wang, and K. Qin, “A visual word weighting scheme based on emerging itemsets for video annotation,” Information Processing Letters, Vol.110, Issue 16, pp. 692-696, 2010.
-  Y. G. Jiang and C. W. Ngo, “Visual word proximity and linguistics for semantic video indexing and near-duplicate retrieval,” Computer Vision and Image Understanding, Vol.113, No.3, pp. 405-414, 2009.
-  J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, “Object retrieval with large vocabularies and fast spatial matching,” In IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1-8, 2007.
-  J. Sivic and A. Zisserman, “Video Google: A text retrieval approach to object matching in videos,” In Ninth IEEE Int. Conf. on Computer Vision, Vol.2, pp. 1470-1477, 2003.
-  L. Zhu, A. B. Rao, and A. Zhang, “Theory of keyblock-based image retrieval,” ACM Trans. on Information Systems, Vol.20, Issue 2, pp. 224-257, 2002.
-  A. Hub, D. Blank, A. Henrich, and W. Müller, “Picadomo: Faceted image browsing for mobile devices,” In Seventh Int. Workshop on Content-Based Multimedia Indexing, pp. 249-254, 2009.
-  C. J. van Rijsbergen, “Information retrieval,” Department of Computing Science University of Glasgow, 1979.
-  E. M. Voorhees and D. M. Tice, “The TREC-8 question answering track evaluation,” In Text Retrieval Conf. TREC-8, pp. 83-105, 1999.
-  D. Arthur and S. Vassilvitskii, “k-means++: The advantages of careful seeding,” In Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027-1035, 2007.
-  M. Friedman, “The use of ranks to avoid the assumption of normality implicit in the analysis of variance,” J. of the American Statistical Association, Vol.32, No.200, pp. 675-701, 1937.
-  F. Wilcoxon, “Individual comparisons by ranking methods,” Biometrics Bulletin, Vol.1, No.6, pp. 80-83, 1945.
-  C. Carson, S. Belongie, H. Greenspan, and J. Malik, “Blobworld: Image segmentation using expectation-maximization and its application to image querying,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.24, Issue 8, pp. 1026-1038, 2002.
-  J. Fauqueur and N. Boujemaa, “Region-based image retrieval: fast coarse segmentation and fine color description,” J. of Visual Languages & Computing, Vol.15, No.1, pp. 69-95, 2004.
-  H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, “SURF: Speeded up robust features,” Computer Vision and Image Understanding, Vol.110, No.3, pp. 346-359, 2008.
-  D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. of Computer Vision, Vol.60, No.2, pp. 91-110, 2004.
-  M. Stricker and M. Orengo, “Similarity of color images,” In Storage and Retrieval for Image and Video Databases III, Vol.2420, pp. 381-392, 1995.
-  J. Laaksonen, E. Oja, M. Koskela, and S. Brandt, “Analyzing lowlevel visual features using content-based image retrieval,” In Int. Conf. on Neural Information Processing, pp. 1333-1338, 2000.
-  S. Brandt, J. Laaksonen, and E. Oja, “Statistical shape features for content-based image retrieval,” J. of Mathematical Imaging and Vision, Vol.17, No.2, pp. 187-198, 2002.
-  B. S. Manjunath, J. R. Ohm, V. V. Vasudevan, and A. Yamada, “Color and texture descriptors,” IEEE Trans. on Circuits and Systems for Video Technology, Vol.11, No.6, pp. 703-715, 2001.
-  ITU, “Studio encoding parameters of digital television for standard 4:3 and wide-screen 16:9 aspect ratios,” ITU-R Recommendation BT.601-6, 2007.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.