An Enhanced Image retrieval Technique based on Edge-Orientation Technique

Ch. Sushmitha, D. Haritha


With the tremendous development in Networking and Multimedia technologies, Image Retrieval plays significant roleand is used for browsing, searching and retrieving images from a large database of digital images. Image Retrieval techniques utilize annotation methods of adding metadata such as captioning, keywords or descriptions to the images. The manual image annotation is much time consuming laborious and expensive.As the data bases size increases, annotation becomes a tedious task. Thus automatic image annotationhas drawn the attention of the researchers in recent years.. The increase in social web application and the semantic web drawn attention of researchers in  the development of several web-based image retrieval tools. This paper presents an easy, efficient image retrieval approach using a new image feature descriptor called Micro-Structure Descriptor (MSD). The microstructures are defined based on an edge orientation similarly while the MSD is built based on the underlying colors in micro-structures with similar edge orientation.In this method of Image retrieval the MSD extracts features by simulating human’s early visual processing by effectively integrating color, texture, color lay out information and shape. The proposed MSD algorithm has high indexing performance and low dimensionalityas it has only 72 dimensions for full color images.The technique is examined on Corel datasets with natural images; the results demonstrate that this image retrieval method is much more efficient and effective than reprehensive feature descriptors, such as Gabor features and Multi Texton Histograms.


Liu et al, “Image retrieval based on multi-textonhistogram”,PATTERN Recognition 43 (7) (2010) 2380-2389.

Huet et al, “Shape recognition from large image libraries by inexact graph matching” Elsevier Pattern Recognition Letters 20 (1999) 1259-1269

Liu et al, “Image retrieval based on the texton co-occurrence matrix” Pattern Recognition 41 (2008) 3521 – 3527.

Dai et al. “Efficient View-Based 3-D Object Retrieval via Hypergraph Learning” tsinghua science and technology ISSN 1007-0214 03/11 pp250-256 Volume 19, Number 3, June 2014

MathiasEitz, Kristian Hildebrand, TamyBoubekeur and Marc Alexa et al, “An evaluation of descriptors for large-scale image retrieval from sketched feature lines” TU Berlin Telecom ParisTech - CNRS LTCI

Fatemi ,Arash , Martin Vetterli et al, “Shapes From Pixels”, ieee transactions on image processing, vol. 25, no. 3, march 2016

MULLER, Henning, et al, “Performance Evaluation in Content-Based Image Retrieval: Overview and Proposals”

R.C.Gonzalez, R.E.Woods, Digital Image Processing, 3rdEdition, Prentice Hall,2007.

Full Text: PDF [Full Text]


  • There are currently no refbacks.

Copyright © 2013, All rights reserved.|

Creative Commons License
International Journal of Science Engineering and Advance Technology is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJSEat , Permissions beyond the scope of this license may be available at