Gradient and Principal Component Analysis Based Texture Recognition System: A Comparative Study

Pradhan, Rohit; Bhutia, Z.G.; Nasipuri, Mita; Pradhan, Mohan P.

doi:10.1109/itng.2008.67

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…PCA is an unsupervised feature selection technique which can transform input features to new ones that probably are better than former [8].Transformed features are formed by first computing the covariance matrix of the original features, and then extracting its eigenvectors [9].…”

Section: Principal Component Analysismentioning

confidence: 99%

Evaluating Feature Selection Techniques in Simulated Soccer Multi Agents System

Farahnakian

Mozayani

2009

2009 International Conference on Advanced Computer Control

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Since the quality of data affects the success rate of data mining and learning algorithms, it is always attempted to identify and remove the irrelevant and redundant information in a dataset. Robotic soccer is a multi-agent system in which agents play in real-time, dynamic, complex and noisy environment. Many parameters affect the result of shooting toward the goal and agents must response to variations in soccer field rapidly. Therefore it is impossible to use all features in scoring behavior. This paper selects dataset for effective features of scoring behavior simulated soccer agents, then compares the size of the trees and accuracy produced by each feature selection scheme against the size of the trees and accuracy produced by C4.5 with no feature selection method. Experimental results have shown that dimensionality reductions lead to operate faster and more effective learning algorithm in real-time simulated soccer agent.

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Section: Principal Component Analysismentioning

confidence: 99%

Evaluating Feature Selection Techniques in Simulated Soccer Multi Agents System

Farahnakian

Mozayani

2009

2009 International Conference on Advanced Computer Control

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“…One of the most commonly encountered problems in computer vision is that of matching appearance. Whether it is images of local features [1], views of objects [2] or faces [3], textures [4] or rectified planar structures (buildings, paintings) [5], the task of comparing appearances is virtually unavoidable in a modern computer vision application. A particularly interesting and increasingly important instance of this task concerns the matching of sets of appearance images, each set containing examples of variation corresponding to a single class.…”

Section: Introductionmentioning

confidence: 99%

Hallucinating optimal high-dimensional subspaces

Arandjelović

2014

Pattern Recognition

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Linear subspace representations of appearance variation are pervasive in computer vision. This paper addresses the problem of robustly matching such subspaces (computing the similarity between them) when they are used to describe the scope of variations within sets of images of different (possibly greatly so) scales. A naïve solution of projecting the lowscale subspace into the high-scale image space is described first and subsequently shown to be inadequate, especially at large scale discrepancies. A successful approach is proposed instead. It consists of (i) an interpolated projection of the low-scale subspace into the highscale space, which is followed by (ii) a rotation of this initial estimate within the bounds of the imposed "downsampling constraint". The optimal rotation is found in the closedform which best aligns the high-scale reconstruction of the low-scale subspace with the reference it is compared to. The method is evaluated on the problem of matching sets of (i) face appearances under varying illumination and (ii) object appearances under varying viewpoint, using two large data sets. In comparison to the naïve matching, the proposed algorithm is shown to greatly increase the separation of between-class and within-class similarities, as well as produce far more meaningful modes of common appearance on which the match score is based.

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“…Whether it is images of local features [7], views of objects [6] or faces [14], textures [13] or rectified planar structures (buildings, paintings) [10], the task of comparing appearances is virtually unavoidable in a modern computer vision application. A particularly interesting and increasingly important instance of this task concerns the matching of sets of appearance images, each set containing examples of variation corresponding to a single class.…”

Section: Introductionmentioning

confidence: 99%

Recognition from Appearance Subspaces across Image Sets of Variable Scale

Arandjelović¹

2010

Procedings of the British Machine Vision Conference 2010

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Linear subspace representations of appearance variation are pervasive in computer vision. In this paper we address the problem of robustly matching them (computing the similarity between them) when they correspond to sets of images of different (possibly greatly so) scales. We show that the naïve solution of projecting the low-scale subspace into the high-scale image space is inadequate, especially at large scale discrepancies. A successful approach is proposed instead. It consists of (i) an interpolated projection of the low-scale subspace into the high-scale space, which is followed by (ii) a rotation of this initial estimate within the bounds of the imposed "downsampling constraint". The optimal rotation is found in the closed-form which best aligns the high-scale reconstruction of the low-scale subspace with the reference it is compared to. The proposed method is evaluated on the problem of matching sets of face appearances under varying illumination. In comparison to the naïve matching, our algorithm is shown to greatly increase the separation of between-class and within-class similarities, as well as produce far more meaningful modes of common appearance on which the match score is based.

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Gradient and Principal Component Analysis Based Texture Recognition System: A Comparative Study

Cited by 3 publications

References 4 publications

Evaluating Feature Selection Techniques in Simulated Soccer Multi Agents System

Evaluating Feature Selection Techniques in Simulated Soccer Multi Agents System

Hallucinating optimal high-dimensional subspaces

Recognition from Appearance Subspaces across Image Sets of Variable Scale

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