International Journal of Advanced and Applied Sciences

Int. j. adv. appl. sci.

EISSN: 2313-3724

Print ISSN: 2313-626X

Volume 3, Issue 12  (December 2016), Pages:  32-36

Title: Class wise image retrieval through scalable color descriptor and edge histogram descriptor

Author(s):  Muhammad Imran 1, *, Rathiah Hashim 2, Aun Irtaz 3, Azhar Mahmood 1, Umair Abdullah 4


1Department of Computer Science, SZABIST-ISB, Islamabad, Pakistan
2FSKTM, University Tun Hussin Onn Malaysia, Johor, Malaysia
3Department of Computer Science, University of Engineering and Technology Taxila, Punjab, Pakistan
4Barani Institute of Information Technology, Rawalpindi, Pakistan

Full Text - PDF          XML


Various domains such as medical science, forensics science and education etc. are generating lot of images on daily bases. As a result of these content generation large image databases are available. These databases are considered as very helpful such as suspects can be searched from forensics database, similarly medical image database can be utilized for the diagnosis purposes. However, proposer management of these databases like, storing and retrieving of images is the demand of the day. Relevant content searching from these databases is a difficult task, however content based image retrieval (CBIR) playing a very important role for searching the relevant contents from these large databases. But this approach is facing some issues. One of the famous issues of CBIR is to describe the image in terms of as feature. This research work aimed is to present a new scheme of image representation by combing the texture and color signature to increase the accuracy of CBIR. Color signatures are generated through Scalable Color Descriptor (SCD) while texture feature are extracted by Edge Histogram Descriptor (EHD). The proposed technique is assessed by testing on the coral image data set and validated by comparing the results with other CBIR approaches. 

© 2016 The Authors. Published by IASE.

This is an open access article under the CC BY-NC-ND license (

Keywords: Content based image retrieval (CBIR), SVM, SCD, Edge histogram descriptor

Article History: Received 18 September 2016, Received in revised form 23 November 2016, Accepted 25 November 2016

Digital Object Identifier:


Imran M, Hashim R, Irtaz A, Mahmood A, and Abdullah U (2016). Class wise image retrieval through scalable color descriptor and edge histogram descriptor. International Journal of Advanced and Applied Sciences, 3(12): 32-36


Chandankhede PH, Puranik PV and Bajaj PR (2011). Soft computing tool approach for texture classification using Discrete Cosine Transform. In the 3rd IEEE International Conference, Electronics Computer Technology (ICECT): 296-299.

Huang PW and Dai SK (2003). Image retrieval by texture similarity. Pattern Recognition, 36(3): 665-679.
Imran M, Hashim R and Khalid NEA (2013). New approach to image retrieval based on color histogram. In the 4th International Conference on Advances in Swarm Intelligence, ICSI, Harbin, China: 453-462.
Imran M, Hashim R and Khalid NEA (2014). Content based image retrieval using MPEG-7 and histogram. In the 1st International Conference on Soft Computing and Data Mining, SCDM, Parit Raja, Batu Pahat, Malaysia: 453-466.
Lai CC and Chen YC (2011). A user-oriented image retrieval system based on interactive genetic algorithm. IEEE Transactions on Instrumentation and Measurement, 60(10): 3318-3325.
Manjunath BS, Ohm JR, Vasudevan VV and Yamada A (2001). Color and texture descriptors. IEEE Transactions on Circuits and Systems for Video Technology, 11(6): 703-715.
Rao MB, Rao BP and Govardhan A (2011a). CTDCIRS: content based image retrieval system based on dominant color and texture features. International Journal of Computer Applications, 18(6): 40-46.
Rao MB, Rao DBP and Govardhan A (2011b). Content based image retrieval using dominant color, texture and shape. International Journal of Engineering Science and Technology (IJEST), 3(4): 2887-2896.
Singha M, Hemachandran K and Paul A (2012). Content-based image retrieval using the combination of the fast wavelet transformation and the colour histogram. IET Image Processing, 6(9): 1221-1226.
Youssef SM (2012). ICTEDCT-CBIR: Integrating curvelet transform with enhanced dominant colors extraction and texture analysis for efficient content-based image retrieval. Computers and Electrical Engineering, 38(5): 1358-1376.
Yu H, Li M, Zhang HJ and Feng J (2002). Color texture moments for content-based image retrieval. In the IEEE Proceedings of International Conference on Image Processing: 929-932.