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Knowledge Based Image Segmentation with Semantic Concepts

N. Shanmugapriya, Dr. S. Pannirselvam

Abstract


Image processing is an innovative area of research in information processing. Within a short duration to holds are steps in every part of human life. Among them image identification and retrieval is the common task to access the desired image from the image databases. Towards its advanced journey image segmentation is a complex as well as and necessary task in knowledge bases image processing system. It segments or divides an image into regions and the labelling of the regions is a challenging problem. In this paper we have analysed various methods on knowledge based semantic segmentation and labelling of images. In this comparative analysis every methods has the vague and complex process. Such complexity motivates to propose a new light weight model to segment the image with necessary conceptual approaches to obtain a fruitful output. The model to be experimented in future with a adopted labelling and segmentation with semantic features in the generation of semantic codebook. This code book will provide and efficient performance in segmentation and also for other related processing.


Keywords


Object Ontology, Domain Knowledge, Sematic Template, Region Growing

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References


Zaher AGHBARI, Akifumi MAKINOUCHI,” Semantic Approach to Image Database Classification and Retrieval”, NII Journal No. 7 ,pp.-8, (2003.9)

A. Abkar, M. A. Sharifi, Likelihood-Based Image Segmentation and Classification: Concepts and Applications, International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B3. Amsterdam 2000,pp.9-16

Ruofei Zhang and Zhongfei (Mark) Zha, Hidden Semantic Concept Discovery in Region Based Image Retrieval, 2004 IEEEInternational Conference on Computer Vision and Pattern Recognition (CVPR’04).

Mingrui Zhang, Lawrence O. Dmitry B. Goldgof, A Generic Knowledge-Guided Image Segmentation and Labeling System Using Fuzzy Clustering Algorithms, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, Vol. 32, No. 5, October 2002 571+-582

P. Kavitha, A. Rama, Construction of Knowledge Structure for Image Segmentation Techniques International Journal of Pure and Applied MathematicsVolume 119 No. 12 2018, 3003-3012ISSN: 1314-3395 (on-line version) 3003-3012

Thanos Athanasiadis Phivos MylonasYannis AvrithisStefanos Kollias, Semantic Image Segmentation and Object Labeling, IEEE Transactions On Circuits and Systems for Video Technology, Vol. 17, No. 3, March 2007,298-312

Hongmei He∗, Tim Watson †, Carsten Maple † Jörn Mehnen‡, Ashutosh Tiwari∗, ,, A New Semantic Attribute Deep Learning with aLinguistic Attribute Hierarchy for Spam Detection, International Joint Conference on Neural Networks, 2017, DOI:10.1109/IJCNN.2017

Jianbing Shen, Jianteng Peng, Xingping Dong, Ling Shao, Fatih Porikli, Higher-Order Energies for Image Segmentation, IEEE Transactions On Image Processing, DOI 10.1109/TIP.2017.2722691, 1057-7149, PP.1-11

Eirikur Agustsson, Jasper R. R. Uijlings, Vittorio Ferrari, Interactive Full Image Segmentation by Considering All Regions Jointly, Interactive Full Image Segmentation by Considering All Regions Jointly, PP11623-11632

Nancy Goyal, Navdeep Singh, A Review on Different Content Based Image Retrieval Techniques Using High Level Semantic Features, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, Issue 7, July 2014, ISSN(Online): 2320-9801 4933-4938

Yi Li1_ Haozhi Qi_ Jifeng Dai Xiangyang Ji1 Yichen Wei, Fully Convolutional Instance-aware Semantic Segmentation, 2359-2367

Carolina Galleguillos Andrew Rabinovich Serge Belongie, Object Categorization using Co-Occurrence, Location and Appearance, Department of Computer Science and Engineering University of California, San Diego, PP 1-8,

G. Forestier1, A. Puissant2, C. Wemmert1, P. Gan_carski, Knowledge-based region labeling for remote sensing image interpretation, This is the author's version of an article published in Computers, Environment and Urban Systems. The _nal authenticated version is available online at: http://dx.doi.org/10.1016/j.compenvurbsys.2012.01.003.,

Anuja Khodaskar, Dr. S.A. Ladke, Content Based Image Retrieval with Semantic Features using Object Ontology, International Journal of Engineering Research & Technology (IJERT)Vol. 1 Issue 4, June – 2012 ISSN: 2278-0181, PP 1-6

Camille Couprie, Cl_ement Farabet, Laurent Najman, Yann LeCun, Toward Real-time Indoor Semantic Segmentation Using Depth Information, Journal of Machine Learning Research 1 (2000) pp.1-48

Abhishek Thakur, Rajeev Ranjan, Image Segmentation and Semantic Labeling using Machine Learning, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-7 Issue-5S2, January 2019,

Ying Liua,∗, Dengsheng Zhanga, Guojun Lua,Wei-Ying Mab, Asurvey of content-based image retrieval with high-level semantics, Pattern Recognition 40 (2007) 262 – 282, 0031-3203/$30.00 _ 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.patcog.2006.04.045

Miss. Ritika Hirwane,Semantic based Image Retrieval,International Journal of Advanced Research in Computer and Communication Engineering, Vol. 6, Issue 4, April 2017 ISSN (Online) 2278-1021, 120-122

Dinesh Jayaraman, Fei Sha ,Kristen Grauman, Decorrelating Semantic Visual Attributes by Resisting the Urge to Share, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014 1-8

Yifan Liu_ Ke Chen Chris Liu Zengchang Qin Zhenbo Luo Jingdong Wang, Structured Knowledge Distillation for Semantic Segmentation, 2604-2613

Varshali Jaiswal, Aruna Tiwari, A Survey of Image Segmentation based on Artificial Intelligence and Evolutionary Approach, IOSR Journal of Computer Engineering (IOSR-JCE), 8727Volume 15, Issue 3 (Nov. - Dec. 2013), PP 71-78, e-ISSN: 2278-0661, p- ISSN: 2278-8727

Wei Xia∗,Csaba Domokos∗,Jian Dong, Loong-Fah Cheong and Shuicheng Yan, Semantic Segmentation without Annotating Segments, 2013 IEEE International Conference on Computer Vision, 1550-5499/13 $31.00 © 2013 IEEE,DOI 10.1109/ICCV.2013.271 PP 2176-2183

Gabriela Csurka, Diane Larlus, Florent Perronnin, what is a good evaluation measure for semantic segmentation? CSURKA, LARLUS, Perronnin: Evaluation of Semantic Segmentation, c 2013. The copyright of this document resides with its authors PP 1-11

Tamil Priya D, Divya Udayan J, A Comprehensive Survey On Various Semantic Based Video/Image Retrieval Techniques, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-6, April 2019


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