Framework for image retrieval using machine learning and statistical similarity matching techniques
Inhaltsverzeichnis
Referenz
Majid Fakheri, Tohid Sedghi, Mahrokh G. Shayesteh, Mehdi Chehel Amirani: Framework for image retrieval using machine learning and statistical similarity matching techniques. Image Processing, IET, vol. 7 issue. 1, pp. 1-11, February 2013.
DOI
http://dx.doi.org/10.1049/iet-ipr.2012.0104
Abstract
The aim of this study is to take advantage of both shape and texture properties of image to improve the performance of image indexing and retrieval algorithm. Further, a framework for partitioning image into non-overlapping tiles of different sizes, which results in higher retrieval efficiency, is presented. In the new approach, the image is divided into different regions (tiles). Then, the energy and standard deviation of Hartley transform coefficients of each tile, which serve as the local descriptors of texture, are extracted as sub-features. Next, invariant moments of edge image are used to record the shape features. The shape features and combination of sub-features of texture provide a robust feature set for image retrieval. The most similar highest priority (MSHP) principle is used for matching of textural features and Canberra distance is utilised for shape features matching. The retrieved image is the image which has less MSHP and Canberra distance from the query image. The proposed method is evaluated on three different image sets, which contain about 17 000 images. The experimental results indicate that the proposed method achieves higher retrieval accuracy than several previously presented schemes, whereas the computational complexity and processing time of the new method are less than those of other approaches.
Extended Abstract
Bibtex
@ARTICLE{6471891,
author={M. Fakheri and T. Sedghi and M. G. Shayesteh and M. C. Amirani},
journal={IET Image Processing},
title={Framework for image retrieval using machine learning and statistical similarity matching techniques},
year={2013},
volume={7},
number={1},
pages={1-11},
keywords={Hartley transforms;computational complexity;image matching;image retrieval;image texture;learning (artificial intelligence);Hartley transform coefficients;MSHP;computational complexity;image indexing;image retrieval algorithm;machine learning;most similar highest priority principle;nonoverlapping tiles;partitioning image;processing time;query image;retrieval efficiency;shape properties;statistical similarity matching techniques;texture properties},
doi={10.1049/iet-ipr.2012.0104},
url={http://dx.doi.org/10.1049/iet-ipr.2012.0104 http://de.evo-art.org/index.php?title=Framework_for_image_retrieval_using_machine_learning_and_statistical_similarity_matching_techniques },
ISSN={1751-9659},
month={February},}
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