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New Non-Parametric Model for Automatic Annotations of Images in Annotation Based Image Retrieval

Kanakam Siva Ram Prasad1

  1. Dept. of IT, Sasi Institute of Technology and Engineering, Tadepalligudem, India.

Correspondence should be addressed to: ksrprasad@sasi.ac.in.


Section:Research Paper, Product Type: Isroset-Journal
Vol.5 , Issue.4 , pp.16-21, Aug-2017


Online published on Aug 30, 2017


Copyright © Kanakam Siva Ram Prasad . 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.
 

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IEEE Style Citation: Kanakam Siva Ram Prasad, “New Non-Parametric Model for Automatic Annotations of Images in Annotation Based Image Retrieval,” International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.16-21, 2017.

MLA Style Citation: Kanakam Siva Ram Prasad "New Non-Parametric Model for Automatic Annotations of Images in Annotation Based Image Retrieval." International Journal of Scientific Research in Computer Science and Engineering 5.4 (2017): 16-21.

APA Style Citation: Kanakam Siva Ram Prasad, (2017). New Non-Parametric Model for Automatic Annotations of Images in Annotation Based Image Retrieval. International Journal of Scientific Research in Computer Science and Engineering, 5(4), 16-21.

BibTex Style Citation:
@article{Prasad_2017,
author = {Kanakam Siva Ram Prasad},
title = {New Non-Parametric Model for Automatic Annotations of Images in Annotation Based Image Retrieval},
journal = {International Journal of Scientific Research in Computer Science and Engineering},
issue_date = {8 2017},
volume = {5},
Issue = {4},
month = {8},
year = {2017},
issn = {2347-2693},
pages = {16-21},
url = {https://www.isroset.org/journal/IJSRCSE/full_paper_view.php?paper_id=431},
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
UR - https://www.isroset.org/journal/IJSRCSE/full_paper_view.php?paper_id=431
TI - New Non-Parametric Model for Automatic Annotations of Images in Annotation Based Image Retrieval
T2 - International Journal of Scientific Research in Computer Science and Engineering
AU - Kanakam Siva Ram Prasad
PY - 2017
DA - 2017/08/30
PB - IJCSE, Indore, INDIA
SP - 16-21
IS - 4
VL - 5
SN - 2347-2693
ER -

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Abstract :
In this paper we propose an automatic approach to annotating and retrieving images based on a training set of images. We assume that regions in an image can be described using a small vocabulary of blobs. Blobs are generated from image features using clustering. Given a training set of images with annotations, the annotation process implemented in our system is based on CMRM. Using a set of annotated images the system learns the joint distribution of the blobs and concepts in this paper show those probabilistic models which allow predicting the probability of generating a word given the blobs in an image. This may be used to automatically annotate and retrieve images given a word as a query. We show that relevance models. Allow us to derive these probabilities in a natural way. Experiments show that the annotation performance of this cross-media relevance model is almost six times as than a model based on word-blob co-occurrence model and twice as good as a state of the art model derived from machine translation.

Key-Words / Index Term :
Automatic Image Annotation, Content Based Image Retrieval, Semantic Gap, Annotation Based Image Retrieval, relevance model

References :
[1] Models J. Jeon, V. Lavrenko and R. Manmatha, Automatic Image Annotation and Retrieval using CrossMediaRelevance, Center for Intelligent Information Retrieval Computer Science department University of Massachusetts Amherst, MA 01003.
[2] RitendraDattaJia Li James Z. Wang, Content-Based Image Retrieval - Approaches and Trends of the New Age, The Pennsylvania State University, University Park, PA 16802, USA datta@cse.psu.edu, jiali@stat.psu.edu, jwang@ist.psu.edu
[3] .K. Barnard and D. Forsyth , Learning the semantics of words and pictures, In International Conference on Computer Vision, Vol.2, pages 408-415, 2001.
[4]. D. Blei, Michael, and M. I. Jordan, Modeling annotated data. To appear in the Proceedings of the 26th annual international ACM SIGIR conference.
[5] K. Barnard, P. Duygulu, N. de Freitas, D. Forsyth, D. Blei, and M. I. Jordan., Matching words and pictures.Journal of Machine Learning Research, 3:1107{1135, 2003}.
[6] HimaliChaudhari et al, A Survey on Automatic Annotation and Annotation Based Image Retrieval. (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2) , 2014, 1368-1371.
[7] R.Ramya. M.E, An Efficient Query Mining Framework Using Spatial Hidden Markov Models for Automatic Annotation of Images. International Journal of Computer Trends and Technology (IJCTT) – volume 11 number 1 – May 2014 ISSN: 2231-2803
[8] A. Agarwal, S.S. Bhadouria “An Evaluation of Dominant Color descriptor and Wavelet Transform on YCbCr Color Space for CBIR” Dept. of Computer Science and Engineeing, NITM, Gwalior, India.International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.56-62, April (2017)

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