• Journal of Korea Multimedia Society
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• v.20 no.3
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• pp.446-455
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• 2017

A color image compression based on saliency map was proposed. The proposed method provides higher quality in saliency blocks on which people's attention focuses, compared with non-saliency blocks on which the attention less focuses at a given bitrate. The proposed method uses 3 different quantization tables according to each block's saliency level. In the experiment using 6 typical images, we compared the proposed method with JPEG and other conventional methods. As the result, it showed that the proposed method (Qup=0.5*Qx) is about 3.1 to 1.2 dB better than JPEG and others in saliency blocks in PSNR at the almost similar bitrate. In the comparison of result images, the proposed one also showed less error than others in saliency blocks.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.965-970
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• 2013

It is important work to extract saliency regions from an image as preprocessing for various image processing methods. In this paper, we introduce an improved method to estimate saliency value of each pixel from an image. The proposed method is an improved work of the previously studied method using color and statistical framework to estimate saliency values. At first, saliency value of each pixel is calculated using the local contrast of an image region at various scales and the most significant saliency pixel is determined using saliency value of each pixel. Then, saliency value of each pixel is again estimated using gauss weight with respect to the most significant saliency pixel and the saliency of each pixel is determined to calculate initial probability. At last, the saliency value of each pixel is calculated by Bayes' rule. The experiments show that our approach outperforms the current statistical based method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.1155-1172
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• 2015

In this paper, we propose a novel saliency detection framework based on illumination invariant features to improve the accuracy of the saliency detection under the different light conditions. The proposed algorithm is divided into three steps. First, we extract the illuminant invariant features to reduce the effect of the illumination based on the local sensitive histograms. Second, a preliminary saliency map is obtained in the CIE Lab color space. Last, we use the region growing method to fuse the illuminant invariant features and the preliminary saliency map into a new framework. In addition, we integrate the information of spatial distinctness since the saliency objects are usually compact. The experiments on the benchmark dataset show that the proposed saliency detection framework outperforms the state-of-the-art algorithms in terms of different illuminants in the images.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1183-1195
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• 2020

Visual saliency detection is an essential task because it is an important part of various vision-based applications. There are many techniques for saliency detection in color images. However, the number of methods for saliency detection in infrared images is limited. In this paper, we introduce a simple approach for saliency detection in infrared images based on the thresholding technique. The input image is thresholded into several Boolean maps, and an initial saliency map is calculated as a weighted sum of the created Boolean maps. The initial map is further refined by using thresholding, morphology operation, and a Gaussian filter to produce the final, high-quality saliency map. The experiment showed that the proposed method has high performance when applied to real-life data.

• Journal of Broadcast Engineering
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• v.20 no.4
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• pp.557-567
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• 2015

Over the past decades, a variety of spatial saliency methods have been introduced. Recently, motion saliency has gained much interests, where motion data estimated from an image sequence are utilized. In general, motion saliency requires reliable motion data as well as image segmentation for producing satisfactory saliency map which poses difficulty in most natural images. To overcome this, we propose a motion-based saliency generation that enhances the spatial saliency based on the combination of spatial and motion saliencies as well as motion complexity without the consideration of complex motion classification and image segmentation. Further, an affinity model is integrated for the purpose of connecting close-by pixels with different colors and obtaining a similar saliency. In experiment, we performed the proposed method on eleven test sets. From the objective performance evaluation, we validated that the proposed method produces better result than spatial saliency based on objective evaluation as well as ROC test.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.615-618
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• 2019

Visual saliency detection is an important part in various vision-based applications. There are a myriad of techniques for saliency detection in color images. However, the number of methods for saliency detection in infrared images is inadequate. In this paper, we introduce a simple approach for pedestrian identification in infrared images using saliency. The input image is thresholded into several Boolean maps, an initial saliency map is then calculated as a weighted sum of created Boolean maps. The initial map is further refined by using thresholding, morphology operation, and Gaussian filter to produce the final, high-quality saliency map. The experiment showed that the proposed method produced high performance results when applied to real-life data.

• Journal of Korea Multimedia Society
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• v.18 no.9
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• pp.1008-1018
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• 2015

In this paper, we propose a method that constructs a saliency map in which important regions are accurately specified and the colors of the regions are less influenced by the similar surrounding colors. Our method utilizes LBP(Local Binary Pattern) histogram information to compare and analyze texture information of surrounding regions in order to reduce the effect of color information. We extract the saliency of stereoscopic images by integrating a 2D saliency map with depth information of stereoscopic images. We then measure the distance between two different sizes of the LBP histograms that are generated from pixels. The distance we measure is texture difference between the surrounding regions. We then assign a saliency value according to the distance in LBP histogram. To evaluate our experimental results, we measure the F-measure compared to ground-truth by thresholding a saliency map at 0.8. The average F-Measure is 0.65 and our experimental results show improved performance in comparison with existing other saliency map extraction methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.53-61
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• 2016

Extracting focused object with saliency map is still remaining as one of the most highly tasked research area around computer vision for it is hard to estimate. Through this paper, we propose enhanced object extraction method based on multi-channel saliency map which could be done automatically without machine learning. Proposed Method shows a higher accuracy than Itti method using SLIC, Euclidean, and LBP algorithm as for object extraction. Experiments result shows that our approach is possible to be used for automatic object extraction without any previous training procedure through focusing on the main object from the image instead of estimating the whole image from background to foreground.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.153-156
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• 2007

인간의 시각 시스템은 선택적 주의 집중에 의해 시각 수용체로 도달되는 많은 물체들 중에서 필요한 정보만을 추출하여 원하는 작업을 수행한다. Itti와 Koch는 시각적 주의를 제어할 수 있는, 신경계를 모방한 계산적 모델을 제안하였으나 조명환경에 고정적인 saliency map을 구성하였다. 따라서, 본 논문에서는 영상에서 ROI(region of interest)을 탐지하기 위한 조명환경에 적응적인 saliency map 모델을 구성하는 기법을 제시한다. 변화하는 환경에서 원하는 특징을 부각시키기 위하여 상황에 적응적인 동적 가중치를 부여한다. 동적 가중치는 conspicuity map에 S.K. Chang이 제안한 PIM(Picture Information Measure)을 적용시켜 정보량을 측정한 후, 이에 따라 정규화된 값을 부여함으로써 구현한다. 제안하는 조명환경에 강인한 적응적인 saliency map 모델 구현의 성능을 얼굴검출 실험을 통하여 검증하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.48-49
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• 2013

Saliency는 인간의 시각에서 관심 영역이나 객체를 찾기 위한 기법으로 최근 영상 리타겟팅, 영상분할 등에 다양하게 활용되고 있다. 기존 제안된 방법들을 전체영상을 대상으로 saliency map을 구하게 되어, 복잡한 객체들의 구성, 큰 전경객체들의 존재 등의 경우에는 성능이 저하되는 문제점이 있다. 따라서 본 논문에서는 배경이 존재하는 영상들을 대상으로 기존 방식중의 하나인 histogram based contrast(HBC)을 개선하는 방법을 제안한다. 배경영역의 빈도확률을 HBC에 적용하여 배경에 존재하는 픽셀값의 saliency을 감소하면, 상대적으로 전경에 존재하는 픽셀들의 saliency는 증가하게 된다. 실험에서는 제안한 기법으로 배경의 saliency는 감소하고, 전경객체는 증가하는 것을 증명하였다.