A neural network based CBIR system using STI features and relevance feedback

  • Authors:

  • K. G. Srinivasa;Karthik Sridharan;P. Deepa Shenoy;K. R. Venugopal;Lalit M. Patnaik

  • Affiliations:

  • Dept. of Comp. Sci. and Eng., Univ., Visvesvaraya Coll. of Eng., Bangalore Univ. Bangalore, K R Circle, 560001, India. Tel.: +91 80 23389518/ Fax: +91 80 22276070/ E-mail: kgsrinivas@msrit.edu;Department of Computer Science and Engineering, Sunny Baffalo, NY, USA. E-mail: karthiksridharan83@yahoo.com;Dept. of Comp. Sci. and Eng., Univ., Visvesvaraya Coll. of Eng., Bangalore Univ. Bangalore, K R Circle, 560001, India. Tel.: +91 80 23389518/ Fax: +91 80 22276070/ E-mail: shenoypd@yahoo.com;Dept. of Comp. Sci. and Eng., Univ., Visvesvaraya Coll. of Eng., Bangalore Univ. Bangalore, K R Circle, 560001, India. Tel.: +91 80 23389518/ Fax: +91 80 22276070/ E-mail: kgsrinivas@msrit.edu, sh ...;Microprocessor Applications Laboratory, Department of CSA, Indian Institute of Science, Bangalore -- 560012, India. E-mail: lalit@micro.iisc.ernet.in

  • Venue:
  • Intelligent Data Analysis
  • Year:
  • 2006

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Abstract

Multimedia mining primarily involves, information analysis and retrieval based on implicit knowledge. The ever increasing digital image databases on the Internet has created a need for using multimedia mining on these databases for effective and efficient retrieval of images. Contents of an image can be expressed in different features such as Shape, Texture and Intensity-distribution(STI). Content Based Image Retrieval(CBIR) is an efficient retrieval of relevant images from large databases based on features extracted from the image. Most of the existing systems either concentrate on a single representation of all features or linear combination of these features. The paper proposes a CBIR System named STIRF (Shape, Texture, Intensity-distribution with Relevance Feedback) that uses a neural network for nonlinear combination of the heterogenous STI features. Further the system is self-adaptable to different applications and users based upon relevance feedback. Prior to retrieval of relevant images, each feature is first clustered independent of the other in its own space and this helps in matching of similar images. Testing the system on a database of images with varied contents and intensive backgrounds showed good results with most relevant images being retrieved for a image query. The system showed better and more robust performance compared to existing CBIR systems.