Pattern-based image retrieval using GLCM

AbstractGray-level co-occurrence matrix (GLCM) is one of the oldest techniques used for texture analysis. It has two important parameters, i.e., distance and direction. In this paper, various combinations of distance and directional angles used for GLCM calculation are analyzed in order to recognize certain patterned images based on their textural features. In the proposed approach, the work is divided into two modules: determining the pattern of the image and pattern retrieval from the dataset. Patterns considered in this paper are horizontal striped, vertical striped, right and left diagonally striped, checkered and other images. For recognizing the pattern, the proposed approach has achieved a percentage accuracy of 96, 98, 96, 90, 96 and 94 for horizontal striped, vertical striped, right and left diagonally striped, checkered and other irregular patterns (not fully stripped), respectively. The proposed approach has a practical implementation in the fashion industry so to filter the search according to the pattern of the cloth.

[1]  D. S. Guru,et al.  Texture Features and KNN in Classification of Flower Images , 2010 .

[2]  Moncef Gabbouj,et al.  Rock Texture Retrieval Using Gray Level Co-occurrence Matrix , 2002 .

[3]  Matti Pietikäinen,et al.  Block-Based Methods for Image Retrieval Using Local Binary Patterns , 2005, SCIA.

[4]  Yong Hu,et al.  Directional Analysis of Texture Images Using Gray Level Co-Occurrence Matrix , 2008, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application.

[5]  Suneeta Agarwal,et al.  An Efficient Image Classification using Bag-of-Words based on SURF and Texture Features , 2017, 2017 14th IEEE India Council International Conference (INDICON).

[6]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Guojun Lu,et al.  Content-based Image Retrieval Using Gabor Texture Features , 2000 .

[8]  K.S. Loke.  Automatic recognition of clothes pattern and motifs empowering online fashion shopping , 2017, 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW).

[9]  Nanik Suciati,et al.  Classification of textile image using support vector machine with textural feature , 2015, 2015 International Conference on Information & Communication Technology and Systems (ICTS).

[10]  Christos Faloutsos,et al.  QBIC project: querying images by content, using color, texture, and shape , 1993, Electronic Imaging.

[11]  A. Gebejes,et al.  Texture Characterization based on Grey-Level Co-occurrence Matrix , 2013 .

[12]  W. Nailon.  Texture Analysis Methods for Medical Image Characterisation , 2010 .

[13]  Iasonas Kokkinos,et al.  Describing Textures in the Wild , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Suneeta Agarwal,et al.  Pipelined technique for image retrieval using texture and color , 2017, 2017 4th International Conference on Power, Control & Embedded Systems (ICPCES).

[15]  Linda G. Shapiro,et al.  Computer and Robot Vision , 1991 .

[16]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..