Multiple Kernel Boosting Saliency Detection of Flame Image of Sintering Section

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

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Multiple Kernel Linear Programming Support Vector Regression Incorporating Prior Knowledge

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2011.00360 ◽

2011 ◽

Vol 37 (3) ◽

pp. 360-370 ◽

Cited By ~ 1

Author(s):

Jin-Zhu ZHOU ◽

Jin HUANG

Keyword(s):

Linear Programming ◽

Support Vector Regression ◽

Prior Knowledge ◽

Support Vector ◽

Multiple Kernel ◽

Programming Support

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Saliency Detection Based on Improved Manifold Ranking via Convex Hull

Journal of Computer-Aided Design & Computer Graphics ◽

10.3724/sp.j.1089.2019.17376 ◽

2019 ◽

Vol 31 (5) ◽

pp. 761

Author(s):

Xiao Lin ◽

Zuxiang Liu ◽

Xiaomei Zheng ◽

Jifeng Huang ◽

Lizhuang Ma

Keyword(s):

Convex Hull ◽

Saliency Detection ◽

Manifold Ranking

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Visual Saliency Detection: An Information Theoretic Algorithm Combined Long-term with Short-term Features

JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY ◽

10.3724/sp.j.1146.2012.01251 ◽

2014 ◽

Vol 35 (7) ◽

pp. 1636-1643

Author(s):

Xiao-liang Qian ◽

Lei Guo ◽

Jun-wei Han ◽

Xin-tao Hu ◽

Gong Cheng

Keyword(s):

Saliency Detection ◽

Visual Saliency ◽

Short Term ◽

Information Theoretic ◽

Visual Saliency Detection

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MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

Current Bioinformatics ◽

10.2174/1574893615999200607173829 ◽

2020 ◽

Vol 15 ◽

Author(s):

Yi Zou ◽

Hongjie Wu ◽

Xiaoyi Guo ◽

Li Peng ◽

Yijie Ding ◽

...

Keyword(s):

Dna Binding ◽

Binding Proteins ◽

Detection Efficiency ◽

Dna Binding Proteins ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Description ◽

Multiple Kernel ◽

Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

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