Salient Region Detection for Biometric Watermarking

Visual saliency, namely the perceptual significance to human vision system (HVS), is a quality that differentiates an object from its neighbors. Detection of salient regions which contain prominent features and represent main contents of the visual scene, has obtained wide utilization among computer vision based applications, such as object tracking and classification, region-of-interest (ROI) based image compression, etc. Specially, as for biometric authentication system, whose objective is to distinguish the identification of people through biometric data (e.g. fingerprint, iris, face etc.), the most important metric is distinguishability. Consequently, in biometric watermarking fields, there has been a great need of good metrics for feature prominency. In this chapter, we present two salient-region-detection based biometric watermarking scenarios, in which robust annotation and fragile authentication watermark are respectively applied to biometric systems. Saliency map plays an important role of perceptual mask that adaptively select watermarking strength and position, therefore controls the distortion introduced by watermark and preserves the identification accuracy of biometric images.

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Multi-Resolution Salient Region Detection in Frequency Domain

International Journal of Advanced Pervasive and Ubiquitous Computing ◽

10.4018/ijapuc.2014010105 ◽

2014 ◽

Vol 6 (1) ◽

pp. 60-70

Author(s):

Yingchun Guo ◽

Yanhong Feng ◽

Gang Yan ◽

Shuo Shi

Keyword(s):

Frequency Domain ◽

Euclidean Distance ◽

Detection Method ◽

Saliency Map ◽

Normal Distribution Function ◽

Salient Region Detection ◽

Salient Region ◽

Region Detection ◽

Proposed Model ◽

Salient Regions

Salient region detection is a challenge problem in computer vision, which is useful in image segmentation, region-based image retrieval, and so on. In this paper we present a multi-resolution salient region detection method in frequency domain which can highlight salient regions with well-defined boundaries of object. The original image is sub-sampled into three multi-resolution layers, and for each layer the luminance and color salient features are extracted in frequency domain. Then, the significant values are calculated by using invariant laws of Euclidean distance in Lab space and the normal distribution function is used to specify the salient map in each layer in order to remove noise and enhance the correlation among the vicinity pixels. The final saliency map is obtained by normalizing and merging the multi-resolution salient maps. Experimental evaluation depicts the promising results from the proposed model by outperforming the state-of-art frequency-tuned model.

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Learning-Based Visual Saliency Model for Detecting Diabetic Macular Edema in Retinal Image

Computational Intelligence and Neuroscience ◽

10.1155/2016/7496735 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 7

Author(s):

Xiaochun Zou ◽

Xinbo Zhao ◽

Yongjia Yang ◽

Na Li

Keyword(s):

Macular Edema ◽

Diabetic Macular Edema ◽

Retinal Image ◽

Visual Saliency ◽

Saliency Map ◽

Support Vector ◽

Salient Region Detection ◽

Region Detection ◽

Saliency Model ◽

Visual Saliency Model

This paper brings forth a learning-based visual saliency model method for detecting diagnostic diabetic macular edema (DME) regions of interest (RoIs) in retinal image. The method introduces the cognitive process of visual selection of relevant regions that arises during an ophthalmologist’s image examination. To record the process, we collected eye-tracking data of 10 ophthalmologists on 100 images and used this database as training and testing examples. Based on analysis, two properties (Feature Property and Position Property) can be derived and combined by a simple intersection operation to obtain a saliency map. The Feature Property is implemented by support vector machine (SVM) technique using the diagnosis as supervisor; Position Property is implemented by statistical analysis of training samples. This technique is able to learn the preferences of ophthalmologist visual behavior while simultaneously considering feature uniqueness. The method was evaluated using three popular saliency model evaluation scores (AUC, EMD, and SS) and three quality measurements (classical sensitivity, specificity, and Youden’sJstatistic). The proposed method outperforms 8 state-of-the-art saliency models and 3 salient region detection approaches devised for natural images. Furthermore, our model successfully detects the DME RoIs in retinal image without sophisticated image processing such as region segmentation.

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Saliency Aggregation: Multifeature and Neighbor Based Salient Region Detection for Social Images

Applied Computational Intelligence and Soft Computing ◽

10.1155/2018/1014595 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16

Author(s):

Ye Liang ◽

Congyan Lang ◽

Jian Yu ◽

Hongzhe Liu ◽

Nan Ma

Keyword(s):

Saliency Detection ◽

Visual Saliency ◽

Salient Region Detection ◽

Salient Region ◽

Region Detection ◽

Image Appearance ◽

Deep Convolution Neural Network ◽

Salient Regions ◽

Social Images ◽

Visual Saliency Models

The popularity of social networks has brought the rapid growth of social images which have become an increasingly important image type. One of the most obvious attributes of social images is the tag. However, the sate-of-the-art methods fail to fully exploit the tag information for saliency detection. Thus this paper focuses on salient region detection of social images using both image appearance features and image tag cues. First, a deep convolution neural network is built, which considers both appearance features and tag features. Second, tag neighbor and appearance neighbor based saliency aggregation terms are added to the saliency model to enhance salient regions. The aggregation method is dependent on individual images and considers the performance gaps appropriately. Finally, we also have constructed a new large dataset of challenging social images and pixel-wise saliency annotations to promote further researches and evaluations of visual saliency models. Extensive experiments show that the proposed method performs well on not only the new dataset but also several state-of-the-art saliency datasets.

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Global-Based Salient Region Detection for Microscopic Image

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.151.607 ◽

2012 ◽

Vol 151 ◽

pp. 607-611

Author(s):

Xiang Jun Liu ◽

Cuixia Bai ◽

Yi Gang Wang

Keyword(s):

Visual Saliency ◽

Spatial Location ◽

Low Complexity ◽

Microscopic Image ◽

Color Information ◽

Salient Region Detection ◽

Salient Region ◽

Region Detection ◽

Important Study ◽

Detection Effect

The salient region detection has been a very important study in machine vision and image analysis. Reliable estimation of visual saliency allows appropriate processing of images without prior knowledge of their contents, and thus remains an important step in many computer vision tasks including image segmentation, object recognition, adaptive compression and image retrieval. This paper presents a global-based contrast region detection method. The color information and the relevance of spatial location were taken into account. Experimental results show that the proposed method compared with the existed methods, our method yielded better detection effect, more precise and low complexity, at the same time, the method was more applicable for salient region detection of microscopic image.

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Salient Region Detection Using Diffusion Process with Nonlocal Connections

Applied Sciences ◽

10.3390/app8122526 ◽

2018 ◽

Vol 8 (12) ◽

pp. 2526 ◽

Cited By ~ 2

Author(s):

Huiyuan Luo ◽

Guangliang Han ◽

Peixun Liu ◽

Yanfeng Wu

Keyword(s):

Saliency Detection ◽

Gaussian Model ◽

Single Layer ◽

Saliency Map ◽

Detection Methods ◽

Sparse Graph ◽

Salient Region Detection ◽

Salient Region ◽

Region Detection ◽

Benchmark Datasets

Diffusion-based salient region detection methods have gained great popularity. In most diffusion-based methods, the saliency values are ranked on 2-layer neighborhood graph by connecting each node to its neighboring nodes and the nodes sharing common boundaries with its neighboring nodes. However, only considering the local relevance between neighbors, the salient region may be heterogeneous and even wrongly suppressed, especially when the features of salient object are diverse. In order to address the issue, we present an effective saliency detection method using diffusing process on the graph with nonlocal connections. First, a saliency-biased Gaussian model is used to refine the saliency map based on the compactness cue, and then, the saliency information of compactness is diffused on 2-layer sparse graph with nonlocal connections. Second, we obtain the contrast of each superpixel by restricting the reference region to the background. Similarly, a saliency-biased Gaussian refinement model is generated and the saliency information based on the uniqueness cue is propagated on the 2-layer sparse graph. We linearly integrate the initial saliency maps based on the compactness and uniqueness cues due to the complementarity to each other. Finally, to obtain a highlighted and homogeneous saliency map, a single-layer updating and multi-layer integrating scheme is presented. Comprehensive experiments on four benchmark datasets demonstrate that the proposed method performs better in terms of various evaluation metrics.

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Appearance-Based Salient Regions Detection Using Side-Specific Dictionaries

Sensors ◽

10.3390/s19020421 ◽

2019 ◽

Vol 19 (2) ◽

pp. 421 ◽

Cited By ~ 2

Author(s):

Mian Fareed ◽

Qi Chun ◽

Gulnaz Ahmed ◽

Adil Murtaza ◽

Muhammad Asif ◽

...

Keyword(s):

Visual Information ◽

Saliency Detection ◽

Saliency Map ◽

Salient Region Detection ◽

Smart Systems ◽

Salient Region ◽

Region Detection ◽

Saliency Maps ◽

Image Saliency ◽

High Level

Image saliency detection is a very helpful step in many computer vision-based smart systems to reduce the computational complexity by only focusing on the salient parts of the image. Currently, the image saliency is detected through representation-based generative schemes, as these schemes are helpful for extracting the concise representations of the stimuli and to capture the high-level semantics in visual information with a small number of active coefficients. In this paper, we propose a novel framework for salient region detection that uses appearance-based and regression-based schemes. The framework segments the image and forms reconstructive dictionaries from four sides of the image. These side-specific dictionaries are further utilized to obtain the saliency maps of the sides. A unified version of these maps is subsequently employed by a representation-based model to obtain a contrast-based salient region map. The map is used to obtain two regression-based maps with LAB and RGB color features that are unified through the optimization-based method to achieve the final saliency map. Furthermore, the side-specific reconstructive dictionaries are extracted from the boundary and the background pixels, which are enriched with geometrical and visual information. The approach has been thoroughly evaluated on five datasets and compared with the seven most recent approaches. The simulation results reveal that our model performs favorably in comparison with the current saliency detection schemes.

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