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Spectral / Phase-based Visual Saliency 1.0

  Date Added: April 28, 2013  |  Visits: 225

Spectral / Phase-based Visual Saliency

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This package provides implementations of several spectral (FFT,DCT)saliency algorithms for images.There are several methods (multichannel_method) to calculate themultichannel saliency:'fft': by default the same as 'fft:whitening''fft:whitening' Uses spectral whitening to calculate the saliency of each channel separately and then averages the result.'fft:residual' Uses the spectral residual to calculate saliency of each channel separately and then averages the result.'dct' Uses DCT-based image signatures to calculate saliency of each channel separately and then averages the result.'quat:fft': Converts the image into a quaternion-based representation, uses quaternion FFT/IFFT operations.'quat:dct' Converts the image into a quaternion-based representation, uses quaternion DCT/IDCT operations.'quat:dct:fast' Same as 'quad:dct', but with a fixed image resolution of 64x48 and uses optimized .mex files for faster calculation.Usage examples:- spectral_saliency_multichannel(imread(..image path...)) or as an example for other color spaces (e.g. ICOPP, Lab, ...)- spectral_saliency_multichannel(rgb2icopp(imread(..image path...)))If you use any of this work in scientific research or as part of alarger software system, you are requested to cite the use in anyrelated publications or technical documentation. The work is basedupon: B. Schauerte, and R. Stiefelhagen, "Quaternion DCT Spectral Saliency: Predicting Human Gaze using Quaternion DCT Image Signatures and Face Detection," in IEEE Workshop on Applications of Computer Vision (WACV), 2012.Notes:- The implementation of the quaternion-based approach requires the quaternion toolbox for Matlab (QTFM).- I kept the implementations as focused and simple as possible and thus they lack more advanced functionality, e.g. more complex normalizations. However, I think that the provided functionality is more than sufficient for (a) people who want to get started in the field of visual attention (especially students), (b) practitioners who have heard about the spectral approach and want to try it, and (c) people who just need a fast, reliable, well-established visual saliency algorithm (with a simple interface and not too many parameters) for their applications.- GBVS and Itti require the original GBVS Matlab implementation by J. Harel (see http://www.klab.caltech.edu/~harel/share/gbvs.php)For more details on the method see:[1] X. Hou and L. Zhang, "Saliency Detection: A Spectral Residual Approach", in CVPR, 2007. (original paper)[2] C. Guo, Q. Ma, and L. Zhang, "Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform," in CVPR, 2008. (extension to quaternions; importance of the residual)[3] X. Hou, J. Harel, and C. Koch, "Image Signature: Highlighting sparse salient regions," in PAMI, 2011. (uses DCT-based "image signatures")[4] B. Schauerte, and R. Stiefelhagen, "Quaternion DCT Spectral Saliency: Predicting Human Gaze using Quaternion DCT Image Signatures and Face Detection," in IEEE Workshop on Applications of Computer Vision (WACV) / IEEE Winter Vision Meetings, 2012. (extension to quaternions; spectral saliency and face detection; evaluation of spectral saliency approaches on eye-tracking data; achieved the currently best reported results on the CERF/FIFA eye-tracking data set and Toronto/Bruce-Tsotsos data set)It has been applied quite a lot during the last years, e.g., see:[5] B. Schauerte, B. Kuehn, K. Kroschel, R. Stiefelhagen, "Multimodal Saliency-based Attention for Object-based Scene Analysis," in IROS, 2011. ("simple" multi-channel and quaternion-based; Isophote-based saliency map segmentation)[6] B. Schauerte, J. Richarz, G. A. Fink,"Saliency-based Identification and Recognition of Pointed-at Objects," in IROS, 2010. (uses multi-channel on intensity, blue-yellow/red-green opponent)[7] B. Schauerte, G. A. Fink, "Focusing Computational Visual Attention in Multi-Modal Human-Robot Interaction," in Proc. ICMI, 2010 (extended to a multi-scale and neuron-based approach that allows to incorporate information about the visual search target)However, the underlying principle has been addressed long before:[9] A. Oppenheim and J. Lim, "The importance of phase in signals," in Proc. IEEE, vol. 69, pp. 529-541, 1981

Requirements: No special requirements
Platforms: Matlab
Keyword: Detection Extension Highlighting Importance Phase Quaternions Quotimage Signature Spectrum Transformquot
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License: Shareware Size: 102.4 KB
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