scholarly journals Visual information retrieval using deep learning with visual attention model

2016 ◽  
Vol 7 (3) ◽  
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Visual Attention ◽  
Visual Information ◽  
Visual Information Retrieval ◽  
Visual Attention Model ◽  
Attention Model

scholarly journals Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Na Li ◽  
Xinbo Zhao ◽  
Yongjia Yang ◽  
Xiaochun Zou
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Visual Attention ◽  
Convolutional Neural Network ◽  
Visual Information ◽  
Local Features ◽  
Classification Method ◽  
Semantic Features ◽  
Visual Attention Model ◽  
Attention Model

Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.

scholarly journals Vehicle license plate recognition using visual attention model and deep learning

2015 ◽  
Vol 24 (3) ◽  
pp. 033001 ◽  
Author(s):  
Di Zang ◽  
Zhenliang Chai ◽  
Junqi Zhang ◽  
Dongdong Zhang ◽  
Jiujun Cheng
Keyword(s):  
Deep Learning ◽  
Visual Attention ◽  
License Plate ◽  
License Plate Recognition ◽  
Visual Attention Model ◽  
Attention Model ◽  
Vehicle License Plate Recognition

A driver visual attention model. Part 1. Conceptual framework

2004 ◽  
Vol 31 (3) ◽  
pp. 463-472 ◽  
Author(s):  
Clark Lim ◽  
Tarek Sayed ◽  
Francis Navin
Keyword(s):  
Visual Attention ◽  
Visual Information ◽  
External Control ◽  
Control Mechanisms ◽  
Driver Behaviour ◽  
General Direction ◽  
Visual Attention Model ◽  
Attention Model ◽  
Age Related ◽  
Attention Demand

This paper describes a driver visual attention model that gathers information based on a selective process so that events such as distractions can be modelled. This model contains visual information gathering capabilities and visual attention mechanisms based on subjective and objective factors. As the research focused on applicability, the model's framework was designed to be integrated as a component processor within a microscopic computer traffic simulation. The model determines visual attention using two mechanisms: internal and external focusing. The internal focusing mechanism is a proactive attention director. This subjective-based mechanism moves the head and eye to a general direction such that information relevant to the current task is actively searched for based on the driver's expectancy. The external focusing mechanism is a reactive attention director based on the characteristics of the objects within the driver's visual field. External control allows for distractions to be modelled, since irrelevant information may objectively demand higher attention than relevant information. For each visible object, these two control mechanisms determine its attention demand value (ADV). Visual information from the object with the highest ADV is then acquired. The ADV also plays a role in determining the information processing time and amount of attention allocated to driving. With the use of this model and its input of various internal and external variables, it is hoped that a variety of driver types with varying visual abilities (age-related, intoxicated) can be simulated within visually detailed environments.Key words: driver behaviour, visibility, driver visual attention, attention demand value, driver simulation models

Fast Visual Attention Model Algorithm Based on Approximate Gaussian Pyramids

2009 ◽  
Vol 20 (12) ◽  
pp. 3240-3253 ◽  
Author(s):  
Guo-Min ZHANG ◽  
Jian-Ping YIN ◽  
En ZHU ◽  
Ling MAO
Keyword(s):  
Visual Attention ◽  
Visual Attention Model ◽  
Attention Model ◽  
Model Algorithm
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