Home   >   CSC-OpenAccess Library   >    Manuscript Information
Content Based Image Retrieval using Color Boosted Salient Points and Shape features of an image
Hiremath P.S, Jagadeesh Pujari
Pages - 10 - 17     |    Revised - 15-02-2008     |    Published - 30-02-2008
Volume - 2   Issue - 1    |    Publication Date - February 2008  Table of Contents
Color saliency, Local descriptors, Gradient vector flow field
Salient points are locations in an image where there is a significant variation with respect to a chosen image feature. Since the set of salient points in an image capture important local characteristics of that image, they can form the basis of a good image representation for content-based image retrieval (CBIR). Salient features are generally determined from the local differential structure of images. They focus on the shape saliency of the local neighborhood. Most of these detectors are luminance based which have the disadvantage that the distinctiveness of the local color information is completely ignored in determining salient image features. To fully exploit the possibilities of salient point detection in color images, color distinctiveness should be taken into account in addition to shape distinctiveness. This paper presents a method for salient points determination based on color saliency. The color and texture information around these points of interest serve as the local descriptors of the image. In addition, the shape information is captured in terms of edge images computed using Gradient Vector Flow fields. Invariant moments are then used to record the shape features. The combination of the local color, texture and the global shape features provides a robust feature set for image retrieval. The experimental results demonstrate the efficacy of the method.
CITED BY (54)  
1 Shi, J., Yan, Q., Xu, L., & Jia, J. Hierarchical Image Saliency Detection on Extended CSSD.
2 Sun, X., Su, A., Chen, S., Yu, Q., & Liu, X. (2016). Objectness to assist salient object detection. IET Image Processing, 10(5), 391-397.
3 Zhang, Y. Y., Wang, Z. P., & Lv, X. D. (2016). Saliency Detection via Sparse Reconstruction Errors of Covariance Descriptors on Riemannian Manifolds. Circuits, Systems, and Signal Processing, 1-18.
4 Wu, X., Du, M., Chen, W., & Wang, J. (2016). Salient object detection via region contrast and graph regularization. Science China Information Sciences, 1-14.
5 Dong, X., Liao, M., Gao, X., & Lin, J. (2015, August). Texture Separation for Saliency Detection of Image with Cluttered Background. In Frontier of Computer Science and Technology (FCST), 2015 Ninth International Conference on (pp. 71-75). IEEE.
6 Imran, M., Hashim, R., & Elaiza, N. (2015). Content Based Image Retrieval Using Color Layout Descriptor and Generic Fourier Descriptor. In Advanced Computer and Communication Engineering Technology (pp. 163-170). Springer International Publishing.
7 DUAN, L., & KONG, L. (2015). Salient Region Detection with Hierarchical Image Abstraction. JOURNAL OF INFORMATION SCIENCE AND ENGINEERING, 31, 861-878.
8 Barya, N., & Jaiswal, H. (2015). Survey on Content based Image Retrieval to Deal with Rapid Growth of Digital Images. International Journal of Computer Applications, 124(12).
9 Liu, Y., Cai, Q., Zhu, X., Cao, J., & Li, H. (2015, September). Saliency detection using two-stage scoring. In Image Processing (ICIP), 2015 IEEE International Conference on (pp. 4062-4066). IEEE.
10 Fadillah, A., & Tjokorda Agung, B. W. implementasi dan analisis salient point detector berdasarkan wavelet pada cbir (content based image retrieval) implementation and analysis wavelet-based salient point detector in cbir (content based image retrieval).
11 Chathurani, N. W. U. D., Geva, S., & Chandran, V. Conversion of an Image to a Document Using Grid-based Decomposition for Efficient Content-Based Image Retrieval.
12 Jhansirani, S., & Kumari, V. V. (2015, March). Improved Hill Climbing Based Segmentation (IHCBS) technique for CBIR system. In Electrical, Computer and Communication Technologies (ICECCT), 2015 IEEE International Conference on (pp. 1-10). IEEE.
13 Anh, D. N. Smooth Context based Color Transfer.
14 Yan, Q., Shi, J., Xu, L., & Jia, J. (2014). Hierarchical Saliency Detection on Extended CSSD. arXiv preprint arXiv:1408.5418.
15 Kong, L., Duan, L., Yang, W., & Dou, Y. (2014). Salient region detection: an integration approach based on image pyramid and region property. IET Computer Vision, 9(1), 85-97.
16 Jayanthi, L., & Lakshmi, K. (2014, February). An improved content based image retrieval using three region colour, straight-line and outline signatures of the image. In Information Communication and Embedded Systems (ICICES), 2014 International Conference on (pp. 1-6). IEEE.
17 Vimina, E. R., & Jacob, K. P. (2014). An Evaluation of Image Matching Algorithms for Region Based Image Retrieval. International Journal of Advancements in Computing Technology, 6(6), 75.
18 Hu, X., Zhang, H., Chen, H., Wang, H., & Sun, M. (2014, November). A crude to fine method to detect salient region. In SPIE/COS Photonics Asia (pp. 92730S-92730S). International Society for Optics and Photonics.
19 Zhou, L., & Yang, Z. (2014, July). Salient region detection based on spatial and background priors. In Information and Automation (ICIA), 2014 IEEE International Conference on (pp. 262-266). IEEE.
20 Yang, H. Y., Li, Y. W., Li, W. Y., Wang, X. Y., & Yang, F. Y. (2014). Content-based image retrieval using local visual attention feature. Journal of Visual Communication and Image Representation, 25(6), 1308-1323.
21 Bai, X., & Wang, W. (2014). Principal pixel analysis and SVM for automatic image segmentation. Neural Computing and Applications, 1-14.
22 Imran, M., Hashim, R., & Khalid, N. E. A. (2014). Content Based Image Retrieval Using MPEG-7 and Histogram. In Recent Advances on Soft Computing and Data Mining (pp. 453-465). Springer International Publishing.
23 Sudha, K. L., Redkar, M., Ranganathan, A., & Upadhyay, K. (2014). An Improvised Approach to Content based Image Retrieval. International Journal of Computer Applications, 106(17).
24 Bhatti, A., Butt, S. M., & Butt, M. M. (2014). visual feature extraction for content-based image retrieval. Science International, 26(1).
25 Chaudhary, M. D., & Upadhyay, A. B. (2014, March). Integrating shape and Edge Histogram Descriptor with Stationary Wavelet Transform for Effective Content Based Image Retrieval. In Circuit, Power and Computing Technologies (ICCPCT), 2014 International Conference on (pp. 1522-1527). IEEE.
26 Imran, M., Hashim, R., & Abdul Khalid, N. E. (2014). Improving the performance of content based image retrieval through color layout descriptor.
27 Lou, J., Ren, M., & Wang, H. (2014). Regional Principal Color Based Saliency Detection.
28 Imran, M., Hashim, R., & Khalid, N. E. A. (2014). Color Histogram and First Order Statistics for Content Based Image Retrieval. In Recent Advances on Soft Computing and Data Mining (pp. 153-162). Springer International Publishing.
29 Butt, S., & Tariq, M. (2013). Visual feature extraction for content-based image retrieval. Int J Acad Sci Res, 1(3), 1-10.
30 Karpagam, V., & Rangarajan, R. (2013). Improved content-based classification and retrieval of images using support vector machine. CURRENT SCIENCE, 105(9), 1267-1275.
31 Tarjoman, M., Fatemizadeh, E., & Badie, K. (2013). a framework for content-based human brain magnetic resonance images retrieval using saliency map. Biomedical Engineering: Applications, Basis and Communications, 25(04), 1350045.
32 Meena, S. M., & Shetty, S. S. (2013, August). Trace transform based identifier for speech based image retrieval on mobile phones. In Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on (pp. 967-972). IEEE.
33 Imran, M., Hashim, R., & Khalid, N. E. A. (2013). New approach to image retrieval based on color histogram. In Advances in Swarm Intelligence (pp. 453-462). Springer Berlin Heidelberg.
34 Vimina, E. R., & Jacob, K. P. (2013). A sub-block based image retrieval using modified integrated region matching. arXiv preprint arXiv:1307.1561.
35 Yan, Q., Xu, L., Shi, J., & Jia, J. (2013, June). Hierarchical saliency detection. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on (pp. 1155-1162). IEEE.
36 Vilvanathan, K., & Rangaswamy, R. (2013). Bi-level classification of color indexed image histograms for content based image retrieval. Journal of Computer Science, 9(3), 343.
37 Vimina, E. R., & Jacob, K. P. (2012). CBIR using local and global properties of image sub-blocks. Int. J. Adv. Sci. Technol, 48, 11-22.
38 Karpagam, V., & Rangarajan, R. (2012). A simple and competent system for content based retrieval of images using color indexed image histogram combined with discrete wavelet decomposition. Eur. J. Sci. Res, 73, 278-290.
39 Chowdhury, M., Das, S., & Kundu, M. K. (2012). Novel CBIR system based on ripplet transform using interactive neuro-fuzzy technique. ELCVIA: electronic letters on computer vision and image analysis, 11(1), 1-13.
40 Das, S., & Kundu, M. K. (2012). Interactive content based image retrieval using Ripplet transform and fuzzy relevance feedback. In Perception and Machine Intelligence (pp. 243-251). Springer Berlin Heidelberg.
41 Desai, P. D., Pujari, J., & Yaligar, N. (2012). Shape based features extracted using Wavelet decomposition. International Journal of Advanced Research in Computer Science, 3(3).
42 Syam, B., & Srinivasa Rao, Y. (2012). An effective similarity measure via genetic algorithm for Content-Based Image Retrieval with extensive features. International Journal of Signal and Imaging Systems Engineering, 5(1), 18-28.
43 Jalab, H. A., & Hasan, A. M. (2012, May). Image retrieval system based on wavelet network. In Computer, Information and Telecommunication Systems (CITS), 2012 International Conference on (pp. 1-4). IEEE.
44 Kumar, D., & Rai, Y. A Survey of Image Retrieval using Support Vector Machine with ACOGA.
45 Rautmare, S., & Bhalchandra, A. Visual Perception Oriented CBIR envisaged through Fractals and Presence Score.
46 Jalab, H. (2011, September). Image retrieval system based on color layout descriptor and Gabor filters. In Open Systems (ICOS), 2011 IEEE Conference on (pp. 32-36). IEEE.
47 Ida, H., Mochammad, H., Ketut, I., & Purnama, E. (2011). Content based image retrieval berdasarkan fitur bentuk menggunakan metode gradient vector flow snake. telematika, (17).
48 Kekre, H. B., & Mishra, D. (2010). Performance Comparison of Density Distribution and Sector mean of sal and cal functions in Walsh Transform Sectors as Feature Vectors for Image Retrieval. International Journal Of Image Processing (IJIP), 4(3), 205.
49 Mohamed, A. S. S. (2010). From content-based to semantic image retrieval. Low level feature extraction, classification using image processing and neural networks, content based image retrieval, hybrid low level and high level based image retrieval in the compressed DCT domain (Doctoral dissertation, University of Bradford).
50 Kekre, H. B., & Sarode, T. (2010). Two Level Vector Quantization Method for Codebook Generation using Kekre’s Proportionate Error Algorithm. International Journal of Image Processing, 4(1), 1-10.
51 Syam, B., & Rao, Y. S. (2010, April). Integrating contourlet features with texture, color and spatial features for effective image retrieval. In Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on (pp. 289-293). IEEE.
52 Vira, N., & Vira, S. (2009). Detection of a Virtual Passive Pointer. International Journal of Image Processing (IJIP), 3(2), 55.
53 Wang, P. C. (2009). Performance Improvement of Vector Quantization with Bit-parallelism Hardware. International Journal of Image Processing (IJIP), 3(4), 152.
54 Hastuti, I. content based image retrieval berdasarkan fitur bentuk menggunakan gvf snake.
1 Google Scholar 
2 ScientificCommons 
3 Academic Index 
4 CiteSeerX 
5 refSeek 
6 iSEEK 
7 Socol@r  
8 ResearchGATE 
9 Bielefeld Academic Search Engine (BASE) 
10 OpenJ-Gate 
11 Scribd 
12 WorldCat 
13 SlideShare 
15 PdfSR 
. J. Li, J.Z. Wang, and G. Wiederhold, "IRM: Integrated Region Matching for Image Retrieval," in Proc. of the 8th ACM Int. Conf. on Multimedia, pp. 147-156, Oct. 2000.
A.K.Jain and Vailalya,, "Image retrieval using color and shape", pattern recognition, vol. 29, pp. 1233-1244, 1996.
C. Carson, S. Belongie, H. Greenspan, and J. Malik, "Blobworld: Image Segmentation Using Expectation-Maximization & Its Application to Image Querying," in IEEE Trans. On PAMI, vol. 24, No.8, pp. 1026-1038, 2002.
C. Harris and M. Stephens, "A combined corner and edge detectors", 4th Alvey Vision Conference, pp. 147-151, 1988.
Chenyang Xu, Jerry L Prince, "Snakes,Shapes, and Gradient Vector Flow", IEEE Transactions on Image Processing, Vol-7, No 3,PP 359-369, March 1998.
D.Lowe, "Distinctive image features from scale invariant keypoints", International Journal of Computer vision, vol. 2(6),pp.91-110,2004.
Dengsheng Zhang, Guojun Lu, "Review of shape representation and description techniques", Pattern Recognition Vol. 37, pp 1-19, 2004.
J. van de Weijer, Th. Gevers, J-M Geusebroek, "Boosting Color Saliency in Image Feature Detection", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27 (4), April 2005.
K.Mikolajezyk and C.Schmid, "Scale and affine invariant interest point detectors", International Journal of Computer Vision, vol. 1(60),pp. 63-86, 2004.
P. Howarth and S.Ruger, "Robust texture features for still-image retrieval", IEE. Proceedings of Visual Image Signal Processing, Vol. 152, No. 6, December 2005.
QTian, Y. Wu and T. Huang, “Combine user defined region-of-interest and spatial layout for image retrieval”, ICIP 2000.
R. Rahamani, S. Goldman, H. Zhang, J. Krettek and J. Fritts, “Localized content-based image retrieval”, ACM workshop on Multimedia Image Retrieval, pp. 227-236, 2005.
Ritendra Datta, Dhiraj Joshi, Jia Li and James Wang, "Image Retrieval: Ideas, Influences, and Trends of the New Age", Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval, November 10-11, 2005, Hilton, Singapore.
T. Gevers and A.W.M. Smeuiders., "Combining color and shape invariant features for image retrieval", Image and Vision computing, vol.17(7),pp. 475-488 , 1999.
Y. Chen and J. Z. Wang, "A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval," in IEEE Trans. On PAMI, vol. 24, No.9, pp. 1252-1267, 2002.
Mr. Hiremath P.S
- India
Mr. Jagadeesh Pujari
- India