Stride Towards Developing an CBIR System Based on Image Annotations and Extensive Multimodal Feature Set
P. Rachana1 , S. Ranjitha2 , H.N. Suresh3
Section:Research Paper, Product Type: Journal Paper
Volume-2 ,
Issue-1 , Page no. 18-22, Jan-2014
Online published on Feb 04, 2014
Copyright © P. Rachana, S. Ranjitha, H.N. Suresh . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
View this paper at Google Scholar | DPI Digital Library
How to Cite this Paper
- IEEE Citation
- MLA Citation
- APA Citation
- BibTex Citation
- RIS Citation
IEEE Style Citation: 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.
MLA Style Citation: 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 2.1 (2014): 18-22.
APA Style Citation: P. Rachana, S. Ranjitha, H.N. Suresh, (2014). Stride Towards Developing an CBIR System Based on Image Annotations and Extensive Multimodal Feature Set. International Journal of Computer Sciences and Engineering, 2(1), 18-22.
BibTex Style Citation:
@article{Rachana_2014,
author = {P. Rachana, S. Ranjitha, H.N. Suresh},
title = {Stride Towards Developing an CBIR System Based on Image Annotations and Extensive Multimodal Feature Set},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {1 2014},
volume = {2},
Issue = {1},
month = {1},
year = {2014},
issn = {2347-2693},
pages = {18-22},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=33},
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=33
TI - Stride Towards Developing an CBIR System Based on Image Annotations and Extensive Multimodal Feature Set
T2 - International Journal of Computer Sciences and Engineering
AU - P. Rachana, S. Ranjitha, H.N. Suresh
PY - 2014
DA - 2014/02/04
PB - IJCSE, Indore, INDIA
SP - 18-22
IS - 1
VL - 2
SN - 2347-2693
ER -
VIEWS | XML | |
4329 | 3902 downloads | 3920 downloads |
Abstract
Content based Image/Video Retrieval system is a querying system that uses content as a key for the retrieval process. It is a difficult task to design an automatic retrieval system because real world images usually contain very complex objects and color information. In this paper, we discuss some of the key contributions in the current decade related to image retrieval and automated image annotation. We also discuss some of the key challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. so nowadays the content based image retrieval are becoming a source of exact and fast retrieval. In this paper the techniques of content based image retrieval are discussed, analyzed and compared. It also introduced the feature like visual descriptor and ontology methods. The suggestion for feature methodology`s to overcome the difficulties and improve the result performance. In this paper we provide an overview of approaches to CBIR. Major approaches to improving retrieval effectiveness via relevance feedback in text retrieval systems are discussed.
Key-Words / Index Term
Inference Mechanisms, Multimedia Databases, Content Based Image Retrieval, Visual Descriptor, Ontology
References
[1]. Demner-Fushman D, Antani SK, Thoma GR, "Automatically Finding Images for Clinical Decision Support," Proceedings of Workshop on Data Mining in Medicine, 7th IEEE Intl Conf on DataMining 2007.
[2]. Demner-Fushman D, Antani SK, Simpson M, Thoma GR, "Combining Medical Domain Ontological Knowledge and Low-level Image Features for Multimedia Indexing," Proc. 2nd International "Language Resources for Content-Based Image Retrieval" Workshop (OntoImage 2008), part of 6th Language Resources and Evaluation Conference (LREC 2008). 20083.
[3]. Daekeun You , Sameer Antani, Dina Demner-Fushman, Md Mahmudur Rahman, Venu Govindaraju , George R. Thoma,� Biomedical Article Retrieval Using Multimodal Features and Image Annotations in Region-based CBIR�.2010
[4]. Antani SK, Demner-Fushman D, Li J, Srinivasan BV, Thoma GR, "Exploring use of images in clinical articles for decision support in Evidence-Based Medicine," Proc. SPIE-IS&T Electronic Imaging. San Jose, CA. January 2008.
[5]. Deserno TM, Antani S, Long R, "Ontology of Gaps in Content-Based Image Retrieval," Journal of Digital Imaging. 2009
[6]. Antani S, Long R, Thoma GR. �Content-Based Image Retrieval for Large Biomedical Image Archives�, Medinfo,San Francisco, CAs, pp.829-33,2004.
[7]. Daekeun You, Emilia Apostolova, Sameer Antani, Dina Demner-Fushman, George R. Thoma,� Figure Content Analysis for Improved Biomedical Article Retrieval�, College of Computing and Digital Media, Vol. 7247, 2009.
[8]. Ryan McDonald,R. Scott Winters,Claire K. Ankuda,Joan A. Murphy,Amy E. Rogers,Fernando Pereira,Marc S. Greenblatt, and Peter S. White,� An Automated Procedure to Identify Biomedical Articles That Contain Cancer-Associated Gene Variants�, 2006
[9]. Manabu Torii, Hongfang Liu,� Classifier ensemble for biomedical document retrieval�,2008.
[10]. Beibei Cheng, Sameer Antani, R. Joe Stanley, Dina Demner-Fushman, George R. Thoma,� Automatic segmentation of subfigure image panels for multimodal biomedical document retrieval�,2011
[11]. Daekeun You , Sameer Antani, Dina Demner-Fushman, Md Mahmudur Rahman, Venu Govindaraju , George R. Thoma, � Automatic identification of ROI infigure images toward improving hybrid (text and image) biomedical document retrieval�,2011.
[12]. H. B. Kekre and Dhirendra Mishra, �Four Walsh Transform Sectors Feature Vectors for Image Retrieval from Image Databases," International Journal of Computer Science and Information Technologies, Vol. 1, No.2, pp 33-37, 2010.
[13]. Serge Belongie, Chad Carson, Hayit Greenspan and Jitendra Malik, �Color and Texture-Based Segmentation using EM and its Application to Content-Based Image Retrieval,� In Proc. of the Sixth International Conference on Computer Vision, Vol. 10, pp. 675-682, Jan 1998.
[14]. Jorma Laaksonen, Erkki Oja and Sami Brandt, �Statistical Shape Features in Content-Based Image Retrieval,� In Proc. Of the 15th International Conference on Pattern Recognition, Vol.2, pp. 1062 - 1065, Sep 2000.
[15]. Yong Rui, and Thomas S. Huang, "Image Retrieval: Current Techniques, Promising Directions, and Open Issues," Journal of Visual Communication and Image Representation, Vol. 10, pp. 39-62, Jan 1999.
[16]. Ying Liu, Dengsheng Zhang, Guojun Lu and Wei-Ying Ma, �A Survey of Content-Based Image Retrieval with High-level Semantics,� Journal of Pattern Recognition, Vol. 40, No. 1, pp. 262-282, Jan 2007.
[17]. Amit Jain, Ramanathan Muthuganapathy and Karthik Ramani, �Content-Based Image Retrieval Using Shape and Depth from an Engineering Database,� In Proc. of the Third International Conference on Advances in Visual Computing, Vol.2, pp. 255-264, 2007.
[18]. Yong Rui, Huang T.S, Ortega M and Mehrotra, "Relevance feedback: a power tool for interactive content-based image retrieval," IEEE Transactions on Circuits and Systems for Video Technology, Vol. 8, No. 5, pp. 644-655, Sep 1998.
[19]. B. Sathyabama, S.Mohana valli, S.Raju and V.Abhai Kumar, "Content Based Leaf Image Retrieval (CBLIR) Using Shape, Color and Texture Features,� Indian Journal of Computer Science and Engineering (IJCSE), Vol. 2, No. 2, May 2011
[20]. Ch. Srinivasa Rao, S.Srinivas Kumar and B.Chandra Mohan, �Content Based Image Retrieval Using Exact Lengendre Moments And Support Vector Machine," The International Journal of Multimedia & Its Applications, Vol. 2, No. 2, pp. 69- 79, May 2010