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

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.

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

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.

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

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 .

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.

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

V. SIMULATIONS

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三、 Application

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

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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