Abstract
Detecting an interest point in the images to extract the features from it is an important step in many computer vision applications. For good performance, these points have to be robust against any transformation that can be done on the images such as viewpoint change, scaling change, rotation, and illumination and, etc. Many of the suggested interest point detectors are measuring the pixel-wise differences in the image intensity or image color. Lee and Chen [1] used image histogram representation instead of pixel representation to detect the interest points. They used the gradient histogram and the RGB color histogram representation. In this work, different color model’s histogram representation such as Ohta-color histogram, HSV-color histogram, Opponent color histogram and Transformed-color histogram are implemented and used in the proposed interest point detector. These detectors are evaluated by measuring their repeatability and matching score between the detected points in the image matching task and the classification accuracy in the image classification task. It is found that as compared with intensity pixels detectors and Lee’s histogram detectors, the proposed histogram detectors performed better under some image conditions such as illumination change, blur and some other conditions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Wei-Ting, L., Hwann-Tzong, C.: Histogram-based interest point detectors. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1590–1596 (2009)
Tuytelaars, T.: Dense interest points. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2281–2288 (2010)
Nowak, E., Jurie, F., Triggs, B.: Sampling Strategies for Bag-of-Features Image Classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 490–503. Springer, Heidelberg (2006)
Bosch, A., Zisserman, A., Munoz, X.: Image Classification using Random Forests and Ferns. In: IEEE 11th Conference on Computer Vision, pp. 1–8 (2007)
Harris, C., Stephens, M.: A Combined Corner and Edge Detection. In: Proceedings of The Fourth Alvey Vision Conference, pp. 147–151 (1988)
Montesinos, P., Gouet, V., Deriche, R., Pelé, D.: Matching color uncalibrated images using differential invariants. Image and Vision Computing 18, 659–671 (2000)
Lindeberg, T.: Feature Detection with Automatic Scale Selection. Int. J. Comput. Vision 30, 79–116 (1998)
Mikolajczyk, K., Schmid, C.: A Performance Evaluation of Local Descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1615–1630 (2005)
Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Found. Trends. Comput. Graph. Vis. 3, 177–280 (2008)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: British Machine Vision Conference, pp. 384–393 (2002)
Tuytelaars, T., Van Gool, L., D’Haene, L., Koch, R.: Matching of affinely invariant regions for visual servoing. In: IEEE International Conference on Robotics and Automation, pp. 1601–1606 (1999)
Kadir, T., Zisserman, A., Brady, M.: An Affine Invariant Salient Region Detector. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 228–241. Springer, Heidelberg (2004)
Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of Interest Point Detectors. Int. J. Comput. Vision 37, 151–172 (2000)
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A Comparison of Affine Region Detectors. Int. J. Comput. Vision 65, 43–72 (2005)
Szeliski, R.: Computer Vision: Algorithms and Applications, 1st edn. Springer, Heidelberg (2011)
Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: IEEE Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 142–149 (2000)
Ohta, Y., Kanade, T., Sakai, T.: Color Information for Region Segmentation. Computer Graphics and Image Processing 13, 222–241 (1980)
Oliva, A., Torralba, A.: Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope. Int. J. Comput. Vision 42, 145–175 (2001)
Boiman, O., Shechtman, E., Irani, M.: In defense of Nearest-Neighbor based image classification. In: IEEE Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)
van der Sande, K., Gevers, T., Snoek, C.: Evaluating Color Descriptors for Object and Scene Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1582–1596 (2010)
Rassem, T.H., Khoo, B.E.: Object class recognition using combination of color SIFT descriptors. In: IEEE Imaging Systems and Techniques (IST), pp. 290–295 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rassem, T.H., Khoo, B.E. (2011). New Color Image Histogram-Based Detectors. In: Badioze Zaman, H., et al. Visual Informatics: Sustaining Research and Innovations. IVIC 2011. Lecture Notes in Computer Science, vol 7066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25191-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-25191-7_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25190-0
Online ISBN: 978-3-642-25191-7
eBook Packages: Computer ScienceComputer Science (R0)