Component based feature space partition and combination in multiple colour spaces for texture classification

2005 ◽  
Author(s):  
S. Chindaro
Keyword(s):  
Texture Classification ◽  
Feature Space ◽  
Space Partition

A Method of the Image Emotion Notation Based Fuzzy Reasoning

2011 ◽  
Vol 179-180 ◽  
pp. 226-232
Author(s):  
Hai Fang Li ◽  
Xiao Yan Qiao ◽  
Li Huan Men
Keyword(s):  
Fuzzy Inference ◽  
Texture Feature ◽  
Feature Space ◽  
Regions Of Interest ◽  
Eye Tracker ◽  
Space Partition ◽  
Logic Network ◽  
Data Imprecision ◽  
Neural Logic ◽  
Neural Logic Network

In this paper, we firstly get the Regions of Interest (ROI) using the Eye tracker and divide every image into two regions including ROI and Non- Regions of Interest (Non-ROI). Secondly, we extract the features of the two regions including ROI and Non-ROI, and get the whole features including texture feature and color feature. Finally, a fuzzy inference network model for image emotion notation using neural logic network is presented. The model describes how to classify the images into different emotions, and neural logic network is used to classification. The learning method proposed is multi-featured, and it allows taking into account the possible predictive power of a simultaneously considered feature conjunction. On the other hand, the feature space partition allows a fuzzy representation of the features and data imprecision integration.

scholarly journals Combination of LBP Bin and Histogram Selections for Color Texture Classification

2020 ◽  
Vol 6 (6) ◽  
pp. 53
Author(s):  
Alice Porebski ◽  
Vinh Truong Hoang ◽  
Nicolas Vandenbroucke ◽  
Denis Hamad
Keyword(s):  
Local Binary Pattern ◽  
Texture Classification ◽  
Selection Procedure ◽  
Texture Features ◽  
Feature Space ◽  
Selection Methods ◽  
Texture Descriptor ◽  
Classification Problems ◽  
The Past ◽  
Color Texture

LBP (Local Binary Pattern) is a very popular texture descriptor largely used in computer vision. In most applications, LBP histograms are exploited as texture features leading to a high dimensional feature space, especially for color texture classification problems. In the past few years, different solutions were proposed to reduce the dimension of the feature space based on the LBP histogram. Most of these approaches apply feature selection methods in order to find the most discriminative bins. Recently another strategy proposed selecting the most discriminant LBP histograms in their entirety. This paper tends to improve on these previous approaches, and presents a combination of LBP bin and histogram selections, where a histogram ranking method is applied before processing a bin selection procedure. The proposed approach is evaluated on five benchmark image databases and the obtained results show the effectiveness of the combination of LBP bin and histogram selections which outperforms the simple LBP bin and LBP histogram selection approaches when they are applied independently.

Texture classification based on Markov modeling in wavelet feature space

2000 ◽  
Vol 18 (12) ◽  
pp. 967-973 ◽  
Author(s):  
M.N Shirazi ◽  
H Noda ◽  
N Takao
Keyword(s):  
Texture Classification ◽  
Feature Space ◽  
Markov Modeling

The Utility of an Intermediate Representation of Feature Space: Lessons From Fingerprint Examiners

2012 ◽  
Author(s):  
Tom Busey ◽  
Chen Yu ◽  
Francisco Parada ◽  
Brandi Emerick ◽  
John Vanderkolk
Keyword(s):  
Feature Space ◽  
Intermediate Representation

Context-Driven Proactive Decision Support for Hybrid Teams

AI Magazine ◽  
2019 ◽  
Vol 40 (3) ◽  
pp. 41-57
Author(s):  
Manisha Mishra ◽  
Pujitha Mannaru ◽  
David Sidoti ◽  
Adam Bienkowski ◽  
Lingyi Zhang ◽  
...  
Keyword(s):  
Decision Support ◽  
Situational Awareness ◽  
Feature Space ◽  
Human Interaction ◽  
Superior Performance ◽  
Uncertain Environments ◽  
Cognitive States ◽  
Contextual Elements ◽  
Human Operators ◽  
Smart Machine

A synergy between AI and the Internet of Things (IoT) will significantly improve sense-making, situational awareness, proactivity, and collaboration. However, the key challenge is to identify the underlying context within which humans interact with smart machines. Knowledge of the context facilitates proactive allocation among members of a human–smart machine (agent) collective that balances auto­nomy with human interaction, without displacing humans from their supervisory role of ensuring that the system goals are achievable. In this article, we address four research questions as a means of advancing toward proactive autonomy: how to represent the interdependencies among the key elements of a hybrid team; how to rapidly identify and characterize critical contextual elements that require adaptation over time; how to allocate system tasks among machines and agents for superior performance; and how to enhance the performance of machine counterparts to provide intelligent and proactive courses of action while considering the cognitive states of human operators. The answers to these four questions help us to illustrate the integration of AI and IoT applied to the maritime domain, where we define context as an evolving multidimensional feature space for heterogeneous search, routing, and resource allocation in uncertain environments via proactive decision support systems.

CNN performance dependence on linear image processing

2020 ◽  
Vol 2020 (10) ◽  
pp. 310-1-310-7
Author(s):  
Khalid Omer ◽  
Luca Caucci ◽  
Meredith Kupinski
Keyword(s):  
Image Processing ◽  
Texture Classification ◽  
Full Rank ◽  
Detection Performance ◽  
Ideal Observer ◽  
Training Data ◽  
Image Texture ◽  
Training Images ◽  
Analytic Expressions ◽  
Linear Compression

This work reports on convolutional neural network (CNN) performance on an image texture classification task as a function of linear image processing and number of training images. Detection performance of single and multi-layer CNNs (sCNN/mCNN) are compared to optimal observers. Performance is quantified by the area under the receiver operating characteristic (ROC) curve, also known as the AUC. For perfect detection AUC = 1.0 and AUC = 0.5 for guessing. The Ideal Observer (IO) maximizes AUC but is prohibitive in practice because it depends on high-dimensional image likelihoods. The IO performance is invariant to any fullrank, invertible linear image processing. This work demonstrates the existence of full-rank, invertible linear transforms that can degrade both sCNN and mCNN even in the limit of large quantities of training data. A subsequent invertible linear transform changes the images’ correlation structure again and can improve this AUC. Stationary textures sampled from zero mean and unequal covariance Gaussian distributions allow closed-form analytic expressions for the IO and optimal linear compression. Linear compression is a mitigation technique for high-dimension low sample size (HDLSS) applications. By definition, compression strictly decreases or maintains IO detection performance. For small quantities of training data, linear image compression prior to the sCNN architecture can increase AUC from 0.56 to 0.93. Results indicate an optimal compression ratio for CNN based on task difficulty, compression method, and number of training images.

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