Efficient Content Based Image Retrieval Using Color and Texture

Image classification is perhaps the most important part of digital image analysis. Retrieval patternbased learning is the most effective that aim to establish the relationship between the current and previous query sessions by analyzing image retrieval patterns. We propose a new feedback based and content based image retrieval system. Content based image retrieval from large resources has become an area of wide interest nowadays in many applications. In this paper we present content-based image retrieval system that uses color and texture as visual features to describe the content of an image region. Our contribution are we use Gabor filters to extract texture features from arbitrary shaped regions separated from an image after segmentation to increase the system effectiveness. In our simulation analysis, we provide a comparison between retrieval results based on features extracted from color the whole image, and features extracted from Texture some image regions. That approach is more effective and efficient way for image retrieval.

[1]  Ole Andreas Flaaten Jonsgård.  Improvements on colour histogram-based CBIR , 2005 .

[2]  S. HiremathP.,et al.  Content Based Image Retrieval using Color Boosted Salient Points and Shape features of an image , 2008 .

[3]  H. B. Kekre,et al.  CBIR using Upper Six FFT Sectors of Color Images for Feature Vector Generation , 2010 .

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

[5]  Pawan Sinha,et al.  A Perceptually Based Comparison of Image Similarity Metrics , 2011, Perception.

[6]  Shengjiu Wang.  A Robust CBIR Approach Using Local Color Histograms , 2001 .

[7]  Béchir el Ayeb,et al.  Survey of the Adequate Descriptor for Content-Based Image Retrieval on the Web: Global versus Local Features , 2009, CORIA.

[8]  Ramesh Babu Durai.  A GENERIC APPROACH TO CONTENT BASED IMAGE RETRIEVAL USING DCT AND CLASSIFICATION TECHNIQUES , 2010 .

[9]  Shaoping Ma,et al.  Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning , 2003, IEEE Trans. Image Process..

[10]  N. Janwe,et al.  CBIR BASED ON COLOR AND TEXTURE , 2011 .

[11]  Moncef Gabbouj,et al.  A regionalized content-based image retrieval framework , 2007, 2007 15th European Signal Processing Conference.

[12]  Edward A. Fox,et al.  A new framework to combine descriptors for content-based image retrieval , 2005, CIKM '05.

[13]  Bernadette Bouchon-Meunier,et al.  A Region-Similarity-Based Image Retrieval System , 2004 .

[14]  Nikolas P. Galatsanos,et al.  A similarity learning approach to content-based image retrieval: application to digital mammography , 2004, IEEE Transactions on Medical Imaging.

[15]  B. S. Adiga,et al.  A Universal Model for Content-Based Image Retrieval , 2008 .

[16]  H. B. Kekre,et al.  CBIR Using Kekre's Transform over Row column Mean and Variance Vectors , 2010 .