An Integrated approach to CBIR using multiple features and HSV Histogram

An efficient search for semantically relevant images has always been thirst in computer vision and processing specially in large scale image retrieval. We propose an integrated approach for fast and effective image retrieval system using multiple features and hsv histogram. Relevance feedback allows user interaction to improve the performance. Features used in this work are improved lbp and modified fourier descriptors, plays vital role in effective retrieval. Experimental results on CALTECH-101 and MPEG CE shape 1 datasets proves that our framework provides better retrieval efficiency compared to the state of art methods. Keywordsemantic retrieval, extended lbp, modified fourier descriptors, similarity distance.

[1]  Shyam Krishna Nagar,et al.  Color Directional Local Quinary Patterns for Content Based Indexing and Retrieval , 2014, Human-centric Computing and Information Sciences.

[2]  Baharum Baharudin,et al.  Analysis of distance metrics in content-based image retrieval using statistical quantized histogram texture features in the DCT domain , 2013, J. King Saud Univ. Comput. Inf. Sci..

[3]  Kazuhiro Kobayashi,et al.  Content-Based Image Retrieval Using Features in Spatial and Frequency Domains , 2015, SOCO 2015.

[4]  Aymeric Histace,et al.  Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation , 2013, Journal of Mathematical Imaging and Vision.

[5]  Tasneem Mirza,et al.  Content based Image Retrieval using Color and Texture , 2016 .

[6]  Maisa Daoud,et al.  Content-Based Image Retrieval Using SOM and DWT , 2015 .

[7]  Muhammad Riaz,et al.  CBIR Based on Adaptive Segmentation of HSV Color Space , 2010, 2010 12th International Conference on Computer Modelling and Simulation.

[8]  James Ze Wang,et al.  Image retrieval: Ideas, influences, and trends of the new age , 2008, CSUR.

[9]  Hui Zhang,et al.  Localized Content-Based Image Retrieval , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Steffen Goebbels,et al.  Fourier descriptors for broken shapes , 2013, EURASIP Journal on Advances in Signal Processing.

[11]  Xiangyang Wang,et al.  An effective image retrieval scheme using color, texture and shape features , 2011, Comput. Stand. Interfaces.