Content factors segmentation with CBIR in real world

World dynamic situation are full of random changes which include multiple factors like audio, video and image. So cause of concurrent incident and simultaneously device performance requirement become typical for information retrieval. That make segmentation approach a method of combining feature transformation with clustering algorithm which is proposed for adequate retrieval of image, that could take input approach equally as similar platform and after function performance of analyses and processing that methodology make image, audio, video information separate and retrieve them as much clear as possible then give a best clear resultant as per certainty.

[1]  Alan F. Smeaton,et al.  Design, implementation and testing of an interactive video retrieval system , 2003, MIR '03.

[2]  Nuno Vasconcelos,et al.  Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Nuno Vasconcelos,et al.  Counting People With Low-Level Features and Bayesian Regression , 2012, IEEE Transactions on Image Processing.

[4]  Nuno Vasconcelos,et al.  Classifying Video with Kernel Dynamic Textures , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Nuno Vasconcelos,et al.  Privacy preserving crowd monitoring: Counting people without people models or tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Sanjeev Khudanpur,et al.  Hidden Markov models for automatic annotation and content-based retrieval of images and video , 2005, SIGIR '05.

[7]  Nuno Vasconcelos,et al.  Probabilistic kernels for the classification of auto-regressive visual processes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Ashok K. Sinha,et al.  A Novel Approach for Content Based Image Retrieval , 2012 .

[9]  Victor R. Lesser,et al.  Multi-agent based peer-to-peer information retrieval systems with concurrent search sessions , 2006, AAMAS '06.

[10]  Howard D. Wactlar,et al.  A system of video information capture, indexing and retrieval for interpreting human activity , 2003, 3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the.

[11]  Minh-Son Dao,et al.  Video retrieval using video object-trajectory and edge potential function , 2004, Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2004..

[12]  Aishy Amer Extraction of high-level video content for advanced video applications , 2003, CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH37436).

[13]  George Tzanetakis,et al.  Music analysis and retrieval systems for audio signals , 2004, J. Assoc. Inf. Sci. Technol..

[14]  Nuno Vasconcelos,et al.  Analysis of Crowded Scenes using Holistic Properties , 2009 .

[15]  Nuno Vasconcelos,et al.  Variational layered dynamic textures , 2009, CVPR.

[16]  A.B. Chan,et al.  Classification and retrieval of traffic video using auto-regressive stochastic processes , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[17]  Pau Baiget,et al.  Determining the best suited semantic events for cognitive surveillance , 2011, Expert Syst. Appl..

[18]  Richard P. Wildes,et al.  Dynamic texture recognition based on distributions of spacetime oriented structure , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  Ju-Jang Lee,et al.  Real-time object tracking and segmentation using adaptive color snake model , 2005, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

[20]  Sagar Soman,et al.  Content Based Image Retrieval using Advanced Color and Texture Features , 2012 .