scholarly journals Saliency Detection Using Sparse and Nonlinear Feature Representation

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Shahzad Anwar ◽  
Qingjie Zhao ◽  
Muhammad Farhan Manzoor ◽  
Saqib Ishaq Khan
Keyword(s):  
Saliency Detection ◽  
Visual Saliency ◽  
Input Image ◽  
Image Features ◽  
Feature Representation ◽  
Weighting Coefficient ◽  
Dual Representation ◽  
Representation Scheme ◽  
Nonlinear Feature ◽  
Sparse Features

An important aspect of visual saliency detection is how features that form an input image are represented. A popular theory supports sparse feature representation, an image being represented with a basis dictionary having sparse weighting coefficient. Another method uses a nonlinear combination of image features for representation. In our work, we combine the two methods and propose a scheme that takes advantage of both sparse and nonlinear feature representation. To this end, we use independent component analysis (ICA) and covariant matrices, respectively. To compute saliency, we use a biologically plausible center surround difference (CSD) mechanism. Our sparse features are adaptive in nature; the ICA basis function are learnt at every image representation, rather than being fixed. We show that Adaptive Sparse Features when used with a CSD mechanism yield better results compared to fixed sparse representations. We also show that covariant matrices consisting of nonlinear integration of color information alone are sufficient to efficiently estimate saliency from an image. The proposed dual representation scheme is then evaluated against human eye fixation prediction, response to psychological patterns, and salient object detection on well-known datasets. We conclude that having two forms of representation compliments one another and results in better saliency detection.

scholarly journals Pre-Processing Filter Reflecting Human Visual Perception to Improve Saliency Detection Performance

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2892
Author(s):  
Kyungjun Lee ◽  
Seungwoo Wee ◽  
Jechang Jeong
Keyword(s):  
Saliency Detection ◽  
Visual Saliency ◽  
Ground Truth ◽  
Bilateral Filter ◽  
Input Image ◽  
Human Visual Perception ◽  
Difference Of Gaussians ◽  
Surrounding Environment ◽  
Benchmark Datasets ◽  
Previous State

Salient object detection is a method of finding an object within an image that a person determines to be important and is expected to focus on. Various features are used to compute the visual saliency, and in general, the color and luminance of the scene are widely used among the spatial features. However, humans perceive the same color and luminance differently depending on the influence of the surrounding environment. As the human visual system (HVS) operates through a very complex mechanism, both neurobiological and psychological aspects must be considered for the accurate detection of salient objects. To reflect this characteristic in the saliency detection process, we have proposed two pre-processing methods to apply to the input image. First, we applied a bilateral filter to improve the segmentation results by smoothing the image so that only the overall context of the image remains while preserving the important borders of the image. Second, although the amount of light is the same, it can be perceived with a difference in the brightness owing to the influence of the surrounding environment. Therefore, we applied oriented difference-of-Gaussians (ODOG) and locally normalized ODOG (LODOG) filters that adjust the input image by predicting the brightness as perceived by humans. Experiments on five public benchmark datasets for which ground truth exists show that our proposed method further improves the performance of previous state-of-the-art methods.

scholarly journals Visual Saliency Prediction Based on Deep Learning

Information ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 257 ◽  
Author(s):  
Bashir Ghariba ◽  
Mohamed S. Shehata ◽  
Peter McGuire
Keyword(s):  
Deep Learning ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Semantic Segmentation ◽  
Input Image ◽  
Human Eye ◽  
Proposed Model ◽  
Global Accuracy ◽  
Visual Saliency Detection ◽  
Deep Learning Model

Human eye movement is one of the most important functions for understanding our surroundings. When a human eye processes a scene, it quickly focuses on dominant parts of the scene, commonly known as a visual saliency detection or visual attention prediction. Recently, neural networks have been used to predict visual saliency. This paper proposes a deep learning encoder-decoder architecture, based on a transfer learning technique, to predict visual saliency. In the proposed model, visual features are extracted through convolutional layers from raw images to predict visual saliency. In addition, the proposed model uses the VGG-16 network for semantic segmentation, which uses a pixel classification layer to predict the categorical label for every pixel in an input image. The proposed model is applied to several datasets, including TORONTO, MIT300, MIT1003, and DUT-OMRON, to illustrate its efficiency. The results of the proposed model are quantitatively and qualitatively compared to classic and state-of-the-art deep learning models. Using the proposed deep learning model, a global accuracy of up to 96.22% is achieved for the prediction of visual saliency.

Interestingness Improvement of Face Images by Learning Visual Saliency

2020 ◽  
Vol 24 (5) ◽  
pp. 630-637
Author(s):  
Dao Nam Anh ◽  
Keyword(s):  
Saliency Detection ◽  
Personal Characteristics ◽  
Visual Saliency ◽  
Image Features ◽  
Machine Learning Techniques ◽  
Consistent Estimation ◽  
Detection Techniques ◽  
Face Images ◽  
Learning Techniques ◽  
Machine Communication

Connecting features of face images with the interestingness of a face may assist in a range of applications such as intelligent visual human-machine communication. To enable the connection, we use interestingness and image features in combination with machine learning techniques. In this paper, we use visual saliency of face images as learning features to classify the interestingness of the images. Applying multiple saliency detection techniques specifically to objects in the images allows us to create a database of saliency-based features. Consistent estimation of facial interestingness and using multiple saliency methods contribute to estimate, and exclusively, to modify the interestingness of the image. To investigate interestingness – one of the personal characteristics in a face image, a large benchmark face database is tested using our method. Taken together, the method may advance prospects for further research incorporating other personal characteristics and visual attention related to face images.

scholarly journals The Study of Randomized Visual Saliency Detection Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Yuantao Chen ◽  
Weihong Xu ◽  
Fangjun Kuang ◽  
Shangbing Gao
Keyword(s):  
Image Segmentation ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Input Image ◽  
Memory Space ◽  
Segmentation Process ◽  
Visual Saliency Detection

Image segmentation process for high quality visual saliency map is very dependent on the existing visual saliency metrics. It is mostly only get sketchy effect of saliency map, and roughly based visual saliency map will affect the image segmentation results. The paper had presented the randomized visual saliency detection algorithm. The randomized visual saliency detection method can quickly generate the same size as the original input image and detailed results of the saliency map. The randomized saliency detection method can be applied to real-time requirements for image content-based scaling saliency results map. The randomization method for fast randomized video saliency area detection, the algorithm only requires a small amount of memory space can be detected detailed oriented visual saliency map, the presented results are shown that the method of visual saliency map used in image after the segmentation process can be an ideal segmentation results.

Sparse Representation of the Human Vision Information and the Saliency Detection Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 3349-3353
Author(s):  
Ju Bo Jin ◽  
Yu Xi Liu
Keyword(s):  
Firing Rate ◽  
Sparse Representation ◽  
Large Scale ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Detection Algorithm ◽  
Human Vision ◽  
Short Term ◽  
Natural Statistics ◽  
Sparse Features

Representation and measurement are two important issues for saliency models. Different with previous works that learnt sparse features from large scale natural statistics, we propose to learn features from short-term statistics of single images. For saliency measurement, we defined basic firing rate (BFR) for each sparse feature, and then we propose to use feature activity rate (FAR) to measure the bottom-up visual saliency. The proposed FAR measure is biological plausible and easy to compute and with satisfied performance. Experiments on human trajectory positioning and psychological patterns demonstrate the effectiveness and robustness of our proposed method.

scholarly journals An Improved Saliency Detection Approach for Flying Apsaras in the Dunhuang Grotto Murals, China

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Zhong Chen ◽  
Shengwu Xiong ◽  
Qingzhou Mao ◽  
Zhixiang Fang ◽  
Xiaohan Yu
Keyword(s):  
Spatial Attention ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Image Features ◽  
Local Contrast ◽  
Color Channel ◽  
Complex Image ◽  
Detection Approach ◽  
Computing Performance ◽  
Visual Saliency Detection

Saliency can be described as the ability of an item to be detected from its background in any particular scene, and it aims to estimate the probable location of the salient objects. Due to the salient map that computed by local contrast features can extract and highlight the edge parts including painting lines of Flying Apsaras, in this paper, we proposed an improved approach based on a frequency-tuned method for visual saliency detection of Flying Apsaras in the Dunhuang Grotto Murals, China. This improved saliency detection approach comprises three important steps: (1) image color and gray channel decomposition; (2) gray feature value computation and color channel convolution; (3) visual saliency definition based on normalization of previous visual saliency and spatial attention function. Unlike existing approaches that rely on many complex image features, this proposed approach only used local contrast and spatial attention information to simulate human’s visual attention stimuli. This improved approach resulted in a much more efficient salient map in the aspect of computing performance. Furthermore, experimental results on the dataset of Flying Apsaras in the Dunhuang Grotto Murals showed that the proposed visual saliency detection approach is very effective when compared with five other state-of-the-art approaches.

scholarly journals Weighted statistical binary patterns for facial feature representation

2021 ◽  
Author(s):  
Hung Phuoc Truong ◽  
Thanh Phuong Nguyen ◽  
Yong-Guk Kim
Keyword(s):  
Comprehensive Evaluation ◽  
Facial Feature ◽  
Input Image ◽  
Feature Representation ◽  
Illumination Variation ◽  
Straight Line ◽  
Mean And Variance ◽  
The Mean ◽  
Face Datasets ◽  
Degraded Images

AbstractWe present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k-th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 98.83% for Caltech, 99.52% for AR, 94.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively. The influence of color spaces and the issue of degraded images are also analyzed with our descriptors. Such a result with theoretical underpinning confirms that our descriptors are robust against noise, illumination variation, diverse facial expressions, and head poses.

scholarly journals CNN-Based Classifier as an Offline Trigger for the CREDO Experiment

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4804
Author(s):  
Marcin Piekarczyk ◽  
Olaf Bar ◽  
Łukasz Bibrzycki ◽  
Michał Niedźwiecki ◽  
Krzysztof Rzecki ◽  
...  
Keyword(s):  
Wavelet Transforms ◽  
Exploratory Study ◽  
Large Scale ◽  
Morphological Difference ◽  
Cosmic Ray ◽  
Input Image ◽  
Image Features ◽  
The Earth ◽  
Cmos Sensor ◽  
Competition Process

Gamification is known to enhance users’ participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various radiation forms that continuously reach the Earth from space, collectively known as cosmic rays. The CREDO Detector app relies on a network of involved users and is now working worldwide across phones and other CMOS sensor-equipped devices. To broaden the user base and activate current users, CREDO extensively uses the gamification solutions like the periodical Particle Hunters Competition. However, the adverse effect of gamification is that the number of artefacts, i.e., signals unrelated to cosmic ray detection or openly related to cheating, substantially increases. To tag the artefacts appearing in the CREDO database we propose the method based on machine learning. The approach involves training the Convolutional Neural Network (CNN) to recognise the morphological difference between signals and artefacts. As a result we obtain the CNN-based trigger which is able to mimic the signal vs. artefact assignments of human annotators as closely as possible. To enhance the method, the input image signal is adaptively thresholded and then transformed using Daubechies wavelets. In this exploratory study, we use wavelet transforms to amplify distinctive image features. As a result, we obtain a very good recognition ratio of almost 99% for both signal and artefacts. The proposed solution allows eliminating the manual supervision of the competition process.

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