Abstract
This chapter explores the automatic methods for implementing pseudo-relevance feedback for retrieval of images and videos. The automation is based on dynamic self-organization, the self-organizing tree map that is capable of identification of relevance in place of human users. The automation process leads to the avoidance of errors in excessive human involvement, and enlarging the size of training set, as compared to traditional relevance feedback. The automatic retrieval system applies for image retrieval in compressed domains (i.e., JPEG and wavelet based coders). In addition, the system incorporates knowledge-based learning to acquire a suitable weighting scheme for unsupervised relevance identification. In the video domain, the pseudo-relevance feedback is implemented by an adaptive cosine network than enhances retrieval accuracy through the network’s forward–backward signal propagation, without user input.
Keywords
- Discrete Cosine Transform
- Discrete Wavelet Transform
- Query Image
- Relevance Feedback
- Discrete Cosine Transform Coefficient
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Yan, R., Hauptmann, A., Jin, R.: Multimedia search with pseudo-relevance feedback. International Conference on Image and Video Retrieval. 238–247 (2003)
Yan, R., Hauptmann, A., Jin, R.: Negative pseudo-relevance feedback in contentbased video retrieval. Proceedings of the Eleventh ACM International Conference on Multimedia. 343–346 (2003)
Kennedy, L. S., Chang, S. F.: A reranking approach for context-based concept fusion in video indexing and retrieval. ACM International Conference on Image and Video Retrieval (Amsterdam, The Netherlands). 333–340 (2007)
Hady, M. F. A., Schwenker, F.: Semi-supervised learning. Handbook of Neural Information Processing. Berlin/Heidelderg: Springer-Verlag. 215–239 (2013)
Joachims, T.: Transductive inference for text classification using support vector machines. International Conference on Machine Learning (Bled, Slovenia). 200–209 (1999)
Wang, L., Chan, K., Zhang, Z.: Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Wisconsin). 629–634 (2003)
Rudinac, S., Larson, M., Hanjalic, A.: Exploiting visual reranking to improve pseudo-relevance feedback for spoken-content-based video retrieval. International Workshop on Image Analysis forMultimedia Interactive Services (London, UK). 17–20 (2009)
Carbonell, J. G., Yang, Y., Frederking, R. E., Brown, R. D., Geng, Y., Lee, D.: Translingual information retrieval: a comparative evaluation. International Joint Conference on Artificial Intelligence (Aichi, Japan) (1997)
Muneesawang, P., Guan, L., Automatic machine interactions for content-based image retrieval using a self-organizing tree map architecture. IEEE Transactions on Neural Network 13 821–834 (2002)
Torjmen, M., Pinel-Sauvagnat, K., Boughanem, M.: Using pseudo-relevance feedback to improve image retrieval results. Workshop of the Cross-Language Evaluation Forum (Budapest, Hungary) 665–673 (2007)
Deselaers, T., Keysers, D., Ney, H.: Fire-flexible image retrieval engine: Image- CLEF 2004 evaluation. Multilingual Information Access for Text, Speech and Images Workshop, Springer 688–698 (2004)
He, R., Zhu, Y., Zhan, W.: Using local latent semantic indexing with pseudo relevance feedback in web image retrieval. International Joint Conference on INC, IMS, and IDC (Seoul, South Korea) 1354–1357 (2009)
El Demerdash, O., Bergler, S., Kosseim, L.: Image query expansion using semantic selectional restrictions. Workshop of the Cross-Language Evaluation Forum (Corfu, Greece) 150–156 (2009)
Kong, H., Guan, L.: Detection and removal of impulse noise by a neural network guided adaptive median filter. Proc. IEEE Int. Conf. on Neural Networks (Perth, Australia) 845–849 (1995)
Kong, H. S.: The Self-Organising Tree Map, and its Applications in Digital Image Processing. PhD Thesis, University of Sydney, Australia (1998)
Randall, J., Guan, L., Zhang, X., Li, W.: Investigations of the self-organizing tree map. Proc. Of Int. Conf. on Neural Information Processing, November 2 724–728 (1999)
Randall, J., Guan, L., Zhang, X., Li, W.: Hierarchical cluster model for perceptual image processing. Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing 1 1041–1044 (2002)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological cybernetics 43 59–69 (1982)
Corel Gallery Magic 65000. http://www.corel.com.Cited15Jan1999
Chen, Q., Petriu, E., Yang, X.: A comparative study of Fourier descriptors and Hu’s seven moment invariants for image recognition. IEEE Canadian Conference on Electrical and Computer Engineering (Niagara Falls, Canada) 103–106 (2004)
Jarrah, K., Kyan, M., Krishnan, S., Guan, L.: Computational intelligence techniques and their applications in content-based image retrieval. IEEE International Conference on Multimedia and Expo (Toronto, Canada) 33–36 (2006)
Lay, J.A., Guan, L.: Image retrieval based on energy histogram of the low frequency DCT coefficients. IEEE Int. Conf. on Accoustic Speech and Signal Processing (Phoenix, USA) 3009–3012 (1999)
Xiong, Z., Huang, T. S.: Subband-based, memory-efficient JPEG2000 images indexing in compressed-domain. IEEE Southwest Symposium on Image Analysis and Interpretation (Santa Fe, USA) (2002)
Bhalod, J., Fahmy, G. F., Panchanathan, S.: Region based indexing in the JPEG2000 framework. Int. Workshop on Content-based Multimedia Indexing (Brescia, Italy) (2001)
ISO/IEC, ISO/IEC 14496-2:1999: Information technology: coding of audio-visual objects - Part 1: visual (1999)
ISO/IEC JTC 1/SC 29/WG 1, ISO/IEC FDIS 15444-1: information technology: JPEG 2000 image coding system: core coding system [WG 1 N 1890] (2000)
Said, A., Pearlman, W. A.: A new and efficient image codec based on set partitioning in hierarchical trees,. IEEE Trans. Circuits Systems Video Technol. 6 243–250 (1996)
Su, P.-C., Wang, H.-J. M., Kuo, C-C.J.: An integrated approach to image watermarking and JPEG-2000 compression. J. of VLSI signal processing systems for signal, image and video technology. 27 35–53 (2001)
Do, M. N., Vertterli, M.: Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance. IEEE Trans. on Image Processing 11 146–158 (2002)
Lui, C., Mandal, M. K.: Fast image indexing based on JPEG2000 packet header. Proc. Int. Workshop on Multimedia Information Retrieval (2001)
Ryan, T.W., Sanders, L. D., Fisher, H. D., Iverson, A. E.: Image compression by texture modeling in the wavelet domain. IEEE Trans. on Image Processing 5 26–36 (1996)
Mandal, M. K., Panchanathan, S., Aboulnasr, T.: Image Indexing Using Translation and Scale-Invariant Moments and Wavelets, Storage and Retrieval for Image and Video Databases (SPIE) 380–389 (1997)
Karayiannis, N. B., Pai, P.-I., Zervos, N.: Image compression based on fuzzy algorithms for learning vector quantization and wavelet image decomposition. IEEE Trans. on Image processing 7 1223–1230 (1998)
Muneesawang, P., Guan, L.: Multiresolution-histogram indexing for wavelet compressed images and relevant feedback learning for image retrieval. IEEE Int. Conf. on Image Processing (Vancouver, Canada) 2 526–529 (2000)
Manjunath, B. S., Ma, W.Y.: Texture features for browsing and retrieval of image data,. IEEE Trans. of Pattern Analysis and Machine Intelligence 18 837–842 (1996)
Manjunath, B. S., Ohm, J.R., Vasudevan, V. V., Yamada, A.: Color and Texture Descriptors. IEEE Trans. on Circuit and Systems for Video Technology 11 703–715 (2001)
Media Graphics International, Photo Gallery 5,000, vol.1 CD-ROM, http://www.mediagraphics.net.Cited1November1999
Maand, W.Y., Manjunath, B. S.: Edge Flow: a framework for boundary detection and image segmentation,. IEEE Int. Conf. on Computer Vision and Pattern Recognition (Puerto Rico) 744–749 (1997)
Wong, H.-S., Guan, L.: Characterization for perceptual importance for object-based image segmentation,. IEEE Int. Conf. on Image Processing (Vancouver, Canada) 54–57 (2000)
Mukherjee, D., Deng, Y., Mitra, S.K.: A region-based video coder using edge Flow segmentation and hierarchical affine region matching. Proc. of SPIE 3309 (1998)
Muneesawang, P., Guan, L.: Image retrieval with embedded sub-class information using Gaussian mixture models,. IEEE Int. Conf. on Multimedia and Expo (Maryland, USA) 1 769–772 (2003)
Naphades, M., Wang, R. R., Huang, T.: Audio-visual query and retrieval: a system that uses dynamic programming and relevance feedback. J. of Electronic Imaging 861–870 (2001)
Wang, R., Naphades, M., Huang, T. S.: Video retrieval and relevance feedback in the context of a post-integration model. IEEE Int. Workshop on Multimedia Signal Processing (Cannes, France) 33–38 (2001)
Wilkinson, R., Hingston, P.: Using the cosine measure in a neural network for document retrieval. ACM SIGIR Conf. on Research and Development in Information Retrieval (Chicago, USA) 202–210 (1991)
Muneesawang, P., Guan, L.: Video retrieval using an adaptive video indexing and automatic relevance feedback. IEEE Trans. on Circuits and Systems on Video Technology 15 1032–1046 (2005)
Chang, S.-F., Sundaram, H.: Structural and semantic analysis of video, Int. Conf. on Multimedia and Expo (New York, USA) 2 687–690 (2000)
Müller, H., Müller, W., Marchand-Maillet, S., Pun, T., Squire, D. M.: Strategies for positive and negative relevance feedback in image retrieval. Int. Conf. on Pattern Recognition, Barcelona, Spain. 1 1043–1046 (2000)
Zhang, H., Smoliar, S. W., Wu, J. H.: Content-based video browsing tools. Multimedia computing and networking 2417 389 (1995)
Informedia Digital Video Library Project at Carnegie Mellon University, http://www.informedia.cs.cmu.edu.Cited2001
Gargi, U., Kasturi, R., Strayer, S.H.: Performance characterization of video-shot-change detection methods. IEEE Trans. on Circuits and Systems for Video Technology 10 1–13 (2000)
Rocchio, J.J.: Relevance feedback in information retrieval, In G. Salton, editor, The SMART Retrieval System–Experiments in Automatic Document Processing. Prentice Hall Inc., Englewood Cliffs, NJ (1971).
K. Wu, K.-H. Yap.: Fuzzy SVM for content-based image retrieval - A pseudo-label support vector machine framework. IEEE Computational Intelligence Magazine, vol.1, 10–16 (2006)
G. Salton, E.A. Fox, E. Voorheers.: Advanced feedback methods in information retrieval. J. of the American Society for Information science, vol. 36, No. 3, 200–210 (1985)
S. Haykin.:, Neural Networks, a Comprehensive Foundation, Prentice Hall, (1999)
M. Antonini, M. Barlaud, P. Mathieu, I. Daubechies.: Image Coding Using Wavelet Transform. IEEE Trans. on Image Process, Vol.1, No. 2, 205–220 (1992)
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Muneesawang, P., Zhang, N., Guan, L. (2014). Self-adaptation in Image and Video Retrieval. In: Multimedia Database Retrieval. Multimedia Systems and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-11782-9_3
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DOI: https://doi.org/10.1007/978-3-319-11782-9_3
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