NDLI: Framework for image retrieval using machine learning and statistical similarity matching techniques

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Framework for image retrieval using machine learning and statistical similarity matching techniques

Content Provider	IET Digital Library
Author	Fakheri, Majid Sedghi, Tohid Shayesteh, Mahrokh G. Amirani, Mehdi Chehel
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.
Starting Page	1
Ending Page	11
Page Count	11
ISSN	17519659
Volume Number	7
e-ISSN	17519667
Issue Number	Issue 1, Feb (2013)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/7/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2012.0104
Journal	IET Image Processing
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Canberra Distance Computational Complexity Computer Vision And Image Processing Technique Database Indexing Edge Detection Edge Image Feature Extraction Hartley Transform Coefficient Hartley Transforms Image Indexing Image Matching Image Partitioning Image Recognition Image Retrieval Image Shape Property Image Texture Image Texture Property Information Retrieval Technique Integral Transforms Learning in AI Machine Learning Most Similar Highest Priority Principle MSHP Principle Nonoverlapping Tiles Query Languages Shape Feature Matching Statistical Similarity Matching Technique SubFeature Textural Feature
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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