報告人:張景舜 p.h. wu, c.c. chen, j.j. ding, c.y. hsu, and y.w. huang ieee transactions on...
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報告人:張景舜P.H. Wu, C.C. Chen, J.J. Ding, C.Y. Hsu, and Y.W. Huang
IEEE Transactions on Image Processing, Vol. 22, No. 9, September 2013
Salient Region Detection Improved by Principle Component Analysis and
Boundary Information
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Outline
① Introduction.② Background③ Framework④ Approach⑤ Simulations⑥ Conclusions
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INTRODUCTION• Many of the existing saliency detection methods
do not pay much attention to the noise problem.
• The framework in this paper adopts both the L0 smoothing filter and PCA to reduce the effect of noise and achieve a better performance.
• the process takes an average of 1.13 seconds per image computed on a 2.8 GHz Intel Core i5 CPU using the released MATLAB code.
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INTRODUCTION
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• Saliency maps color spatial variance border measurement local-global contrast
Top: Original images.
Middle: Saliency maps generated from the proposed method.
Bottom : Human labeled ground truths
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BACKGROUND一、 L0 Smoothing Filter( 一 ) A demonstration of the L0 smoothing filter:
(a) the original image
(b) the smoothed image after removing small-magnitude gradients.
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BACKGROUND( 二 ) An illustration of the L0 smoothing filter.
(a) The intensity of a row from the original image.
(b) The result after applying the L0 smoothing filter. Note that only the prominent intensity changes are preserved.
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二、 Principle Component Analysis(PCA)1. Extract the main colors of images.2. Feature extraction and dimensionality reduction.3. PCA functionalities
(1) noise reduction(2) translation error(3) Attenuation(4)ellipse fitting(5) solving for non-full-rank eigenproblems.
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名詞解釋
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PCA : Principle Component AnalysisKL transform : Karhunen-Loeve transformGMM : Gaussian Mixture ModelCSV : color spatial varianceBM : border measurementBS : boundary scoringCA : Context AwareDis : the central distance mapdatabase : ASD , SED , SOD , BSD
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FRAMEWORK
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IV. APPROACH
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一、 Color Spatial Variance(CSV)
• Color spatial variance based on the GMM is a widely used global feature that matches the human visual system.
• If a color is extensively distributed within an image, it may be the background color.
• In other words, a specific color with a smaller spatial variance will attract greater attention, and is more likely to be part of the salient object.
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IV. APPROACH
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二 . Image Segmentation for Border Measurement
• using the L0 smoothing filter before segmentation can prevent over-segmentation.
• salient objects rarely connect with image borders, an adaptive region-merging method and border measurement
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IV. APPROACH
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三、 PCA Context-Aware1. Dimensionality reduction for decreasing the
computational complexity; 2. Noise reduction;3. Alleviation of translation errors .
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IV. APPROACH
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D. Boundary Scoring
• The salient regions are highlighted more than the background, thus alleviating the effect of an incomplete segment result.
• boundary of each segment to determine the saliency value rather than the entire segment, since the CA method is more effective around edges.
• he utilization of the boundary can lower the influence of an incomplete segmentation result.
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IV. APPROACH
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V. SIMULATIONS
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一、 Performance Measurement1. Precision-recall rate2. F-measure
W and H are the width and height of the saliency map S, respectively.
Ta is twice the mean saliency of the image.
β is set to 0.3
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V. SIMULATIONS
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三、 Application
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VI. CONCLUSIONS
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• Reduce noise and other redundant information
• Increase both the accuracy and efficiency of saliency detection
• The color special variance, border information, and global-local contrast were utilized to construct the saliency maps.
• Our proposed method achieved higher precision-recall rates and F-measure than other state-of-the-art methods
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CONCLUSIONL0 smoothing filter and PCA canreduce noise and other redundant information and increase both the accuracy and efficiency of saliency detection.the color special variance, border information, and global-local contrast were utilized to construct the saliency maps.ASD, SED,and SOD and the simulation results showed this proposed method achieved higher precision-recall rates and F-measurethan other state-of-the-art methods.
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Thanks for listening
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