Design and Development of an Algorithm for Image Clustering In Textile Image Retrieval Using Color Descriptors

All textile industries aim to produce competitive materials and the competi tion enhancement depends mainly on designs and quality of the dresses produced by each industry. Every day, a vast amount of textile images are being generated such as images of shirts, jeans, t -shirts and sarees. A principal driver of innovation is WorldWide Web, unleashing publication at the scale of tens and millions of content creators. Images play an important role as a picture is worth thousand words in the field of textile design and marketing. A retrieving of images needs special concepts such as i mage annotation, context, and image content and image values. This research work aimed at studying the image mining process in detail and analyzes the methods for retrieval. The textile images analyze various methods for clustering the images and developing an algorithm for the same. The retrieval method considered is based on relevance feedback, scalable method, edge histogram and color layout. The image clustering algorithm is designed based on color descriptors and k -means clustering algorithm. A softwar e prototype to prove the proposed algorithm has been developed using net beans integrated development environment and found successful.

[1]  T. Devi,et al.  Identification Of Acute Appendicitis Using Euclidean Distance On Sonographic Image , 2011 .

[2]  Alan F. Smeaton,et al.  TRECVID 2004 Experiments in Dublin City University , 2004, TRECVID.

[3]  James Ze Wang,et al.  System for Screening Objectionable Images Using Daubechies' Wavelets and Color Histograms , 1997, IDMS.

[4]  Limsoon Wong,et al.  DATA MINING TECHNIQUES , 2003 .

[5]  Hayit Greenspan,et al.  Finding Pictures of Objects in Large Collections of Images , 1996, Object Representation in Computer Vision.

[6]  A. K. Pujari,et al.  Data Mining Techniques , 2006 .

[7]  Xu Jinhua,et al.  The Related Techniques of Content-Based Image Retrieval , 2008, 2008 International Symposium on Computer Science and Computational Technology.

[8]  Tao Zhang,et al.  Fast person-specific image retrieval using a simple and efficient clustering method , 2009, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[9]  Yixin Chen,et al.  Content-based image retrieval by clustering , 2003, MIR '03.

[10]  Sanjay Silakari,et al.  Color Image Clustering using Block Truncation Algorithm , 2009, ArXiv.

[11]  Keiji Yanai Web Image Mining toward Generic Image Recognition , 2003, WWW.

[12]  Jalil Abbas,et al.  FRAME WORK FOR CONTENT BASED IMAGE RETRIEVAL (Textual Based) SYSTEM , 2010 .

[13]  E. Merzari,et al.  Large-Scale Simulations on Thermal-Hydraulics in Fuel Bundles of Advanced Nuclear Reactors , 2007 .

[14]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[15]  Cheng-Jye Luh,et al.  Generating page clippings from web search results using a dynamically terminated genetic algorithm , 2005, Inf. Syst..