Salient Region Detection and Analysis Based on the Weighted Band-Pass Features


Researches on visual attention mechanism have revealed that the human visual system (HVS) is sensitive to the higher frequency components where they are distinctive from their surroundings by popping out. These attentive components of the scene can be in any form such as edge to texture differences based on the focus of attention of HVS. There are several visual attention computational models that can yield saliency values of attentive regions on the image. Some of these models take advantage of band-pass filter regions on spatial domain by computing center-surround differences with difference of low pass filters. They use either down-sampling that may cause loss of information or constant scale of the filters that may not contain all the necessary saliency information from the image. Therefore, we proposed an efficient and simple saliency detection model with full resolution and high perceptual quality, which outputs several band-pass regions by utilizing Fourier transform to obtain attentive regions edges to textures from the color image. All these detected important information with different bandwidth, then, were fused in a weighted manner by giving more priority to the texture compared to edge based salient regions. Experimental analysis was made for different color spaces and the model was compared with some relevant state of the art algorithms. As a result, the proposed saliency detection model has promising results based on the area under curve (AUC) performance evaluation metric.

Share and Cite:

N. İmamoğlu, J. Gomez-Tames and W. Yu, "Salient Region Detection and Analysis Based on the Weighted Band-Pass Features," Journal of Software Engineering and Applications, Vol. 6 No. 5B, 2013, pp. 43-48. doi: 10.4236/jsea.2013.65B009.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Y. Fang, W. Lin, B. S. Lee, C. T. Lau, Z. Chen and C. W. Lin, “Bottom-up Saliency Detection Model Based on Human Visual Sensitivity and Amplitude Spectrum,” IEEE Transactions on MultiMedia, Vol. 4, No. 1, 2012, pp. 187-198. doi:10.1109/TMM.2011.2169775
[2] Y. Fang, W. Lin, B.-S. Lee, C. T. Lau and C.-W. Lin, “Bottom-up Saliency Detection Model Based on Amplitude Spectrum,” MMM 2011, LNCS, Vol. 6523, 2011, pp. 370-380. doi:10.1007/978-3-642-17832-0_35
[3] D. Walther and C. Koch, “Modelin Attention to Salient Proto-Objects,” Neural Network, Vol. 19, 2006, pp. 1395-1407. doi:10.1016/j.neunet.2006.10.001
[4] R. Achanta, S. He-mami, F. Estrada and S. Susstrunk, “Frequency-Tuned Salient Region Detection,” IEEE InTernational Conference on Computer Vision and Pattern Recognition, 2009, pp. 1597-1604. doi:10.1109/CVPR.2009.5206596
[5] L. Itti, C. Koch and E. Niebur, “Model of Saliency-Based Visual Attention for Rapid Scene Analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 11, 1998, pp. 1254-1259. doi:10.1109/34.730558
[6] Y. F. Ma and H. J. Zhang, “Contrast-Based Image Attention Analysis by Using Fuzzy Growing,” in Proceeding of ACM International Conference Multimedia, 2003, pp. 374-381.doi:10.1145/957013.957094
[7] X. Hou and L. Zhang, “Saliency Detection: A Spectral Residual Ap-proach,” In Proceeding of IEEE International Conference on Computer Vision and Pattern Recognition, 2007.doi:10.1109/CVPR.2007.383267
[8] N. Murray, M. Vanrell, X. Otazu and C. A. Parraga, “Saliency Estimation Using a Non-parametric Low-level Vision Model,” IEEE International Conference on Computer Vision and Pattern Recognition, 2011, pp. 433-440. doi:10.1109/CVPR.2011.5995506
[9] S. Goferman, L. Zelnik-Manor and A. Tal, “Context-Aware Saliency Detection,” IEEE International Conference on Computer Vision and Pattern Recognition, 2010, pp. 2376-2383.doi:10.1109/CVPR.2010.5539929
[10] G. Car-dillo, “ROC Curve: Compute a Receiver Operating Characteristics Curve,” unpublished, 2008.
[11] R. C. Gonzalez and R. E. Woods, “Digital Image Processing,” 3rd Edition, Pearson prentice Hall, New Jersey, 2008.
[12] R. C. Gonzalez, R. E. Woods and S. L. Eddins, “Digital Image Processing Using Matlab,” 2nd Edition, Gatesmark Publishing, LLC, 2009.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.