A Study on Web Based Image Search by Re-Ranking Techniques
Keywords:
Re-Ranking, Distance Metrics (DM), TBIRAbstract
the continuing growth of online digital photos, video data and image retrieval an active research area. The ever-growing importance of rich visual information in today’s web is evidenced by the popularity of social web image retrieval. I focus primarily on the process of searching and retrieving images using a visual query known as content-based image retrieval our primary aiming is reducing the semantic gap between the low-level visual features and high-level image features. This paper proposed for comprehensive survey In this paper is to focus on the survey of various existing methods and technologies and application along with has been discussed some new clustering techniques, distance metrics methods, also of these various re-ranking methods also addressed.Finally,we have been details discussed ten journals paper.
References
Lixin Duan,Wen Li, Ivor Wai-Hung Tsang,Dong Xu, “Improving Web Image Search by Bag-Based Re-Ranking”, In: IEEE Trans.Syst., Vol. 20, No.11,pp.3280-3290, 2011.
Xinmei Tian,Linjun Yang, Jingdong Wang, Xiuqiung,Xian-Sheng Hua, “Bayesian Visual Re ranking”, In: IEEE Trans.Syst., Vol. 10, No.10.pp.1-13, 2010.
S.Jadav,P.Rawool,V.Shah, “Image merging in transform domain”, Int.J.Sc in network security and communication,Vol.5, Issue.1, pp.38-39, 2017.
Dan Lu, Xiaoxiao Liu, Xueming Qian, “Capturing User Intention for One-Click Internet Image Search”, In: In :IEEE Trans.Syst, Vol. 34, No. 7, pp.119-126, 2012.
Xiaoou Tang,Ke Liu,Xiaoou Tang, “Query Specific Visual Semantic Spaces for Web Image Re-Ranking”, In: IEEE Transaction, USA, pp.857-864,2104.
Daniel Carlos, Guimaraes Pedronette, Ricardo da s.Torres, “Combining Re-Ranking and rank aggregation methods for image retrieval”, Multimedia Tools and Applications, Vol.75, Issue.15, pp.9121-44, 2016.
Xiang-Yang Wang, Lin-lin Liang, Wei-Yi Li, Dong-Ming Li, Hong-Ying Yang, “A new SVM based relevance feedback image retrieval using probabilistic feature and weighted kernel function”, Journal of Visual Communication and Image Representation, Vol.38, Issue.7, pp.256-275, 2016.
Gholam Ali Montazer,Davar Giveki, “Content based image retrieval system using clusterd scale invariant feature transforms”, Optik-International Journal for Light and Electron Optics, Vol.126, Issue.18, pp.1695-1699, 2015.
Vinay Lowanshi , Shweta Shrivastava, “Two Tier Architecture for Content Based Image Retrieval Using Modified SVM and knn-GA”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.41-45, 2014.
M.S. Kumar, A.I. Mercy, "Content Based Image Recovery Approaches Using Graphical Image Recovery Procedure (GIRP)", International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.151-157, 2014.
Quan Wen,M Emre Celebi, “Hard versus fuzzy c-means clustering for color quantization”, EURASIP Journal on Advances in Signal Processing, Vol.2011, Issue.1, pp.118-122, 2011.
Ting Yao,chong-Wah Ngo and To Mei, “Circular Reranking for Visual Search”, In:IEEE Trans.Syst,.Vol.22,No.4 , pp.1644-1655, 2013.
P. Amani and Maddali M. V. M. Kumar, "Conservative Procedures for Web Image Re-Ranking Precisions Using Semantic Signatures", International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.7-10, 2015.
Gunhan park,Yunju Baek,Heung-Kyu Lee, “Re-ranking Algorithm using post-retrieval clustering for content-based image retrieval”, In:Information Processing and Management, vol,41, Issue.2, pp.177-194,2005.
Theo Gevers and Arnold W.M Smeulders, “PicTOSeek:Combining Color and Shape Invariant Features for Image Retrieval”, In:IEEE Trans. Syst.,vol.9, No.1.pp.102-119, 2000.
P. Rachana, S. Ranjitha, H.N. Suresh, "Stride Towards Developing an CBIR System Based on Image Annotations and Extensive Multimodal Feature Set", International Journal of Computer Sciences and Engineering, Vol.2, Issue.1, pp.18-22, 2014.
Tania Di Mascio , Luigi Laura, and Valeria Mirabella, “The Interface of VISTO, a New Vector Image Search Tool”, LNCS Springer, Berlin, pp.417-426, 2007.
C. P. Patidar and Meena Sharma, "Histogram Computations on GPUs Kernel using Global and Shared Memory Atomics", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.4, pp.1-6, 2013.
Songhe Feng, Hong Bao, Congyan Lang, De Xu, “Combining visual attention model with multi-instance learning for tag ranking”, In:Neurocomputing, vol,74, Issue.17, pp.3619-3627,2011.
Shao-Hang Kao,Wei-yen Day,and Pu-Jen Cheng, “An Aesthetic – Based Approach to Re-Ranking Web Images”, LNCS Springer, Berlin, pp.610-623, 2010.
Song Bai, Xiang Bai, “Sparse Contexual Activation for Efficient visual Re-Ranking”, IEEE Transactions on Image Processing , Vol.25,No.3, pp.1056-1069, 2016.
Dan Lu, Xiaoxiao Liu, Xueming Qian, “Tag-Based Image Search by Social Re-Ranking”, In IEEE Trans.Syst on multimedia,Vol,18,No.8,pp.1628-1639, 2016.
Aasish Sipani, Phani Krishna and Sarath Chandra, "Content Based Image Retrieval Using Extended Local Tetra Patterns", International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.11-17, 2014.
Ivan Gonzalez-Diaz, “Neighborhood Matching For Image Retrieval”, In IEEE Trans.Syst on multimedia,Vol.19, Issue.3, pp.1520-9210, 2016.
Tao Mei, Yong Rui, Shipeng Li, Qi Tian, “Multimedia Search Re-Ranking:A Literature Survey”, In ACM Journal Vol.46,No.3, pp.26-38, 2012.
Prashant Srivastava, Ashish Khare, “Integration of wavelet transform,Local binary patterns and moments for content based image retrieval”, Journal of Visual Communication and Image Representation, vol,42, Issue.1, pp. 78-10,2017.
Xiaopeng Yang, Tao Mei ,Yongdong Zhang,Jie Liu,Shin Ichi satoh, “Web Image Search Re-Ranking With Click-based Similarity And Typicality”, In: IEEE Trans.Syst., Vol. 25, No. 5, pp :4617-4630, 2016.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.