Abstract
This paper proposes an interactive colour video segmentation technique based on granular reflex fuzzy min–max neural network (GrRFMN). It is noted that most of the image and video segmentation techniques are pixel based. It means that segmentation is carried out on a pixel-by-pixel basis. In this paper, a novel data granule based approach for colour video segmentation is presented. The proposed technique is capable to process granules of video frames. This results into a faster segmentation process. The video segmentation discussed here is of supervised type. In the training phase, GrRFMN learns different categories in a video frame through an interaction with the user. A trained GrRFMN is then used to segment the subsequent video sequences. The main advantage of using GrRFMN is its capability to learn online in a single pass through data and property to deal with granular data which help to segment a video sequence in an interactive mode. Results of the proposed method on standard images and video sequences are also presented.
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Nandedkar AV, Biswas PK (2009) A reflex fuzzy min max neural network for granular data classification. IEEE Trans Neural Network 20(7):1117–1134
Rosenfeld A, Davis LS (1978) Iterative histogram modification. IEEE Trans Syst Man Cybern 8:300–302
Deshmukh K, Nandedkar AV, Joshi YV, Shinde GN (2004) Multilevel approach for color image segmentation. In: Proceedings of Indian conference on computer vision and graphics, ICVGIP 2004. pp 338–342
Kundu MK, Pal SK (1986) Thresholding for edge detection using human psychovisual phenomena. Pattern Recogn Lett 4:433–441
Pratt WK (1991) Digital image processing, 2nd edn. Wiley-Inter science, New York
Lim YW, Lee SU (1990) On the color image segmentation algorithm based on the thresholding and the fuzzy c-means technique. Pattern Recogn 23(9):935–952
Nickisch H, Rother C, Kohli P, Rhemann C (2010) Learning an interactive segmentation system. In: Proceedings of Indian conference on computer vision and graphics. Chennai, Dec 2010
Chen H, Qi F, Zhang S (2003) Supervised video object segmentation using a small number of interactions. In: IEEE international conference on acoustics, speech, and signal processing, (ICASSP '03), vol 3(3). pp 365–368
Nandedkar A V (2011) An interactive colour video segmentation using granular reflex fuzzy neural network. In: Proceedings of the world congress on engineering 2011. Lecture notes in engineering and computer science. WCE 2011, London, UK, 6–8 July 2011. pp 1688–1693
Pedrycz W (2001) Granular computing: an introduction. In: Proceedings of joint IFSA world congress and 20th NAFIPS international conference, vol 3. pp 1349–1354
Zadeh LA (1997) Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Set Syst 90:111–127
Cheng HD, Jiang XH, Sun Y, Wang J (2001) Colour image segmentation: Advances and propects. Pattern Recogn 34(12):2259–2281
P. Seeling and M. Reisslein in print (2012) Video Transport Evaluation With H.264 Video Traces. IEEE Communications Surveys and Tutorials. Online: DOI 10.1109/SURV.2011.082911.00067: weblink: http://trace.eas.asu.edu
Acknowledgement
The author would like thank AICTE New Delhi for financially supporting the project under Career Award for Young Teachers Scheme.
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Nandedkar, A.V. (2013). An Interactive Colour Video Segmentation: A Granular Computing Approach. In: Ao, SI., Gelman, L. (eds) Electrical Engineering and Intelligent Systems. Lecture Notes in Electrical Engineering, vol 130. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-2317-1_11
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DOI: https://doi.org/10.1007/978-1-4614-2317-1_11
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