R. Revathi
M. Hemalatha
ABSTRACT
A feature is defined as a role of one or more measurements, the values of some irrefutable assets of an object, computed so that it quantifies some significant characteristics of an object. Here in this paper we can have presented a clearer view of how feature extraction takes place on the basis of color, texture, and shape in video processing. The variation that occurs during feature extraction is also shown. The Iteration in which the objects are identified from a specified location is also considered. The proposed algorithm shows that the objects are identified correctly in a lower number of iterations itself when compared to the traditional algorithms.
- Daniela Stan Raicu, "Visual Computing Workshop: Image Processing, DePaul University, ImageFeatureExtraction", May 21st, 2004. http://facweb.cs.depaul.edu/research/vc/VC_Workshop/presentations/pdf/daniela_tutorial2.pdfGoogle Scholar
- Michele Saad, "Low-Level Color and Texture Feature Extraction for Content-Based Image Retrieval", Final Project Report, May 09, 2008.EE 381K: Multi-Dimensional Digital Signal Processing. http://users.ece.utexas.edu/~bevans/courses/ee381k/projects/spring08/saad/FinalProjectReport.pdfGoogle Scholar
- Huiqiong Chen and Qigang Gao, "Integrating Color and Gradient into Real-Time CurveTrackingandFeatureExtractionforVideoSurveillance". http://cdn.intechopen.com/pdfs/13685/InTechIntegrating_color_and_gradient_into_real_time_curve_tracking_and_feature_ extraction_for_video_surveillance.pdfGoogle Scholar
- Shih-Fu Chang and John R. Smith, "SPIE Symposium on Visual Communications and Signal Processing, Extracting Multi-Dimensional Signal Features for Content-BasedVisualQuery". http://www.ee.columbia.edu/ln/dvmm/publications/95/chang95f.pdfGoogle Scholar
- T. Caelli and D. Reye, "On the Classification of Image Regions by Color, Texture, and Shape," Pattern Recognition, Vol. 26, No. 4, pp.461--470, 1993.Google ScholarCross Ref
- S. Mallat, "Zero-Crossing of a Wavelet Transform," IEEE Transactions on Information Theory, Vol. 37, No. 4, July 1991, pp.1019--33. Google ScholarDigital Library
- S. Mallat and S. Zhong, "Characterization of Signals from Multiscale Edges," IEEE T-PAMI, Vol. 14, No. 7, July 1992, pp. 710--32. Google ScholarDigital Library
- E. Saber, A. M. Tekalp, "Integration of color, edge and texture features for automatic region-based image annotation and retrieval," Electronic Imaging, 7, pp. 684--700, 1998.Google ScholarCross Ref
- J. Canny, "A Computational Approach to Edge Detection," IEEE T-PAMI, Vol. PAMI-8, No. 6, Nov. 1986, 679--98. Google ScholarDigital Library
- M. K. Hu, "Visual pattern recognition by moment invariants," IRE Trans. on Information Theory, 8, pp. 179--187, 1962.Google Scholar
- Efficient method for feature extraction on video preocessing
Recommendations
Decorrelation Methods of Texture Feature Extraction
This paper presents the development and evaluation of a visual texture feature extraction method based on a stochastic field model of texture. Results of recent visual texture discrimination experiments are reviewed in order to establish necessary and ...
Relevant Feature Subset Selection from Ensemble of Multiple Feature Extraction Methods for Texture Classification
Performance of texture classification for a given set of texture patterns depends on the choice of feature extraction technique. Integration of features from various feature extraction methods not only eliminates risk of method selection but also brings ...
An Image Retrieval using combined approach Wavelets and Local Binary Pattern
ICIA-16: Proceedings of the International Conference on Informatics and AnalyticsWith the invent of Internet and the availability of efficient image capturing devices such as image scanners, digital cameras and high capacity public networks, cheap storage; the volume of digital images is increasing exponentially. This created a need ...
Comments