Reduction of semantic gap using relevance feedback technique in image retrieval system

This paper proposes a novel content based image retrieval system incorporating the relevance feedback technique. In order to improve the retrieval accuracy of content based image retrieval systems, research focus has been shifted in reducing the semantic gap between visual features and the human semantics. The five major techniques available to narrow down the semantic gap are: (a) Object ontology (b) machine learning (c) relevance feedback (d) semantic template (e) web image retrieval. This paper focuses on the relevance feedback technique by which semantic gap can be reduced in order to improve the retrieval efficiency of the system. The major challenges facing the existing relevance feedback technique is the number of iterations and the execution time. The proposed algorithm provides a better solution to overcome both these challenges. The efficiency of the system can be calculated based on precision and recall.

[1]  B. S. Patil,et al.  Semantic understanding of Image content , 2011 .

[2]  Yueting Zhuang,et al.  Apply semantic template to support content-based image retrieval , 1999, Electronic Imaging.

[3]  Wenyin Liu,et al.  Joint semantics and feature based image retrieval using relevance feedback , 2003, IEEE Trans. Multim..

[4]  Hui Lin,et al.  Remotely sensed image retrieval based on region-level semantic mining , 2012, EURASIP J. Image Video Process..

[5]  Dah-Jye Lee,et al.  Using relevance feedback with short-term memory for content-based spine X-ray image retrieval , 2009, Neurocomputing.

[6]  Thrasyvoulos N. Pappas,et al.  Perceptually based techniques for semantic image classification and retrieval , 2006, Electronic Imaging.

[7]  Qiang Yang,et al.  A unified framework for semantics and feature based relevance feedback in image retrieval systems , 2000, ACM Multimedia.

[8]  Ju-Chin Chen,et al.  Region-based image retrieval system with heuristic pre-clustering relevance feedback , 2010, Expert Syst. Appl..

[9]  Ying Liu,et al.  A survey of content-based image retrieval with high-level semantics , 2007, Pattern Recognit..

[10]  Alaa Mohamed Riad,et al.  Web Image Retrieval Search Engine based on Semantically Shared Annotation , 2012 .

[11]  J. Shanbehzadeh,et al.  Relevance Feedback Optimization in Content Based Image Retrieval Via Enhanced Radial Basis Function Network , 2022 .

[12]  V. Seenivasagam,et al.  Different relevance feedback techniques in CBIR: A survey and comparative study , 2012, 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET).

[13]  Bir Bhanu,et al.  Image retrieval with feature selection and relevance feedback , 2010, 2010 IEEE International Conference on Image Processing.

[14]  Thomas S. Huang,et al.  Relevance feedback in image retrieval: A comprehensive review , 2003, Multimedia Systems.

[15]  Liana Stanescu,et al.  Automatic image annotation and semantic based image retrieval for medical domain , 2013, Neurocomputing.

[16]  Harpreet Kaur,et al.  Survey of Techniques of High Level Semantic Based Image Retrieval , 2013 .

[18]  Francesco G. B. De Natale,et al.  Content-Based Image Retrieval by Feature Adaptation and Relevance Feedback , 2007, IEEE Transactions on Multimedia.

[19]  Qian Liu,et al.  Improving keyword based web image search with visual feature distribution and term expansion , 2009, Knowledge and Information Systems.

[20]  S. Vaishnavi.  CBIR using Relevance Feedback Retrieval System , 2012 .

[21]  Sushmit Mallik,et al.  Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval , 2012, ArXiv.

[22]  Ying Liu,et al.  Region-based image retrieval with high-level semantics using decision tree learning , 2008, Pattern Recognit..

[23]  Yixin Chen,et al.  An unsupervised learning approach to content-based image retrieval , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..

[24]  Fabio A. González,et al.  A Semantic Content-Based Retrieval Method for Histopathology Images , 2008, AIRS.

[25]  Philip S. Yu,et al.  Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns , 2011, IEEE Transactions on Knowledge and Data Engineering.

[26]  Hadi Sadoghi Yazdi,et al.  Learning of Relevance Feedback Using a Novel Kernel Based Neural Network , 2010 .