Driving to a fast IMS feature vector computing

Increasing amount of image data transmitted via Internet has triggered the development of general purposes Image Mining Systems (IMS). An IMS performance relies on a good and fast feature vector specification that describes univocally an entire image. Vector size and the relationship between each evaluated feature and its computation time are critical, moreover when the image amount is big enough. Decreasing this IMS computational complexity by means of parallelism at the different involved tasks is one solution. Nowadays clusters of computers are already widely used as a low cost and high utility option to special-purpose machines, and suited to solve image processing problems with a high degree of data locality and parallelism. At this paper, we will focus on parallelism into the IMS processing stage trying to accelerate the feature vector calculus thru a cluster architecture attempting to give a better performance to the whole image mining system.

[1]  Roman M. Palenichka,et al.  Effective image and video mining: an overview of model-based approaches , 2005, MDM '05.

[2]  Henryk Krawczyk,et al.  A parallel environment for image data mining , 2002, Proceedings. International Conference on Parallel Computing in Electrical Engineering.

[3]  Keiji Yanai,et al.  Generic image classification using visual knowledge on the web , 2003, ACM Multimedia.

[4]  M. Carter.  Computer graphics: Principles and practice , 1997 .

[5]  José Crespo,et al.  Theoretical aspects of morphological filters by reconstruction , 1995, Signal Process..

[6]  Dina Q. Goldin,et al.  Generating fuzzy semantic metadata describing spatial relations from images using the R-histogram , 2004, Proceedings of the 2004 Joint ACM/IEEE Conference on Digital Libraries, 2004..

[7]  Bernd Jähne,et al.  Digital Image Processing: Concepts, Algorithms, and Scientific Applications , 1991 .

[8]  Jorge G. Barbosa,et al.  Parallel Image Processing System on a Cluster of Personal Computers (Best Student Paper Award: First Prize) , 2000, VECPAR.

[9]  Sanjay Ranka,et al.  Parallel Processing for Computer Vision and Image Understanding - Guest Editors' Introduction to the Special Issue , 1992, Computer.

[10]  Selim Aksoy,et al.  Interactive training of advanced classifiers for mining remote sensing image archives , 2004, KDD.

[11]  Steven K. Feiner,et al.  Computer graphics: principles and practice (2nd ed.) , 1990 .

[12]  Qinbao Song,et al.  Integrating color and spatial information for content-based image retrieval in large image database , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[13]  D. Hubel,et al.  Segregation of form, color, movement, and depth: anatomy, physiology, and perception. , 1988, Science.

[14]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[15]  Mikhail A. Vorontsov.  Parallel image processing based on an evolution equation with anisotropic gain: integrated optoelectronic architectures , 1999 .

[16]  Markus A. Stricker,et al.  Similarity of color images , 1995, Electronic Imaging.

[17]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[18]  Edward R. Dougherty,et al.  Morphological methods in image and signal processing , 1988 .

[19]  Leslie G. Valiant,et al.  Bulk synchronous parallel computing-a paradigm for transportable software , 1995, Proceedings of the Twenty-Eighth Annual Hawaii International Conference on System Sciences.

[20]  Jim R. Parker,et al.  Algorithms for image processing and computer vision , 1996 .

[21]  Beng Chin Ooi,et al.  An Evaluation of Color-Spatial Retrieval Techniques for Large Image Databases , 2001, Multimedia Tools and Applications.

[22]  William Nick Street,et al.  Automatic Feature Mining for Personalized Digital Image Retrieval , 2001, MDM/KDD.

[23]  Jacqueline Fernández,et al.  Towards a parallel image mining system , 2007 .

[24]  J. Todd.  Book Review: Digital image processing (second edition). By R. C. Gonzalez and P. Wintz, Addison-Wesley, 1987. 503 pp. Price: £29.95. (ISBN 0-201-11026-1) , 1988 .

[25]  Fillia Makedon,et al.  Generating fuzzy semantic metadata describing spatial relations from images using the R-histogram , 2004, JCDL.

[26]  Prof. Dr. Thomas Bräunl,et al.  Parallel Image Processing , 2001, Springer Berlin Heidelberg.

[27]  Angela Y. Wu,et al.  Parallel Image Processing , 2001 .

[28]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  María Fabiana Piccoli,et al.  Appling parallelism in image mining , 2007 .

[30]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.