Genetic Algorithm Optimization of Adaptive Multi-Scale GLCM Features

Walker, Ross F.; Jackway, Paul; Longstaff, D.

doi:10.1142/s0218001403002228

Cited by 41 publications

(24 citation statements)

References 17 publications

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“…In [49], an extension is introduced, where a new matrix called motif co-occurrence matrix was proposed. Walker et al [50] have proposed to form co-occurrence matrix-based features by weighted summation of cooccurrence matrix elements from localized areas of high discrimination. The GLCM contains the second-order statistical information of spatial relationship of the image pixels.…”

Section: Facial Image Featuresmentioning

confidence: 99%

A Personal Identification Framework based on Facial Image and Fingerprint Fusion Biometric

ElAlami¹,

Amin²,

El-alfi³

2012

IJCA

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Biometric based person identity verification is gaining more and more attention. Several studies have shown that multimodal biometric identification systems improve the recognition accuracy and reliability compared with recognition using a single biometric. The present paper introduces a new personal identification framework that is based on the fusion of face and fingerprint biometrics. The proposed framework overcomes the limitations of face recognition systems as well as fingerprint verification systems. The gray-level co-occurrence matrix and the minutiae extraction are used to represent the features of face and fingerprint image respectively. This framework uses the correlation coefficient as a similarity measure to retrieve the closest face and the corresponding fingerprint images with a query image. Experimental results performed on a given database of face and fingerprint images show that the proposed framework improved greatly the security and recognition rate.

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Section: Facial Image Featuresmentioning

confidence: 99%

A Personal Identification Framework based on Facial Image and Fingerprint Fusion Biometric

ElAlami¹,

Amin²,

El-alfi³

2012

IJCA

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“…The main advantage of wavelets over Fourier analysis is that they provide better resolution in space as well as frequency. In particular, it has emerged as an effective tool for texture analysis, which leads to three well-known texture signatures, namely, the energy signature (Laine and Fan, 1993;Wouwer et al, 1999), the co-occurrence signature (Haralick et al, 1973;Walker et al, 1995Walker et al, , 2003, and the generalized Gaussian density (GGD) signature (Do and Vetterli, 2002;Mallat, 1989;Sharifi and Leon-Garcia, 1995).…”

Section: Starch Grain Featuresmentioning

confidence: 99%

A novel and effective multistage classification system for microscopic starch grain images

Choy

Tong

Zhao

2009

Microscopy Res & Technique

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This article presents a novel and effective multistage system for classifying Chinese Materia Medica microscopic starch grain images. The proposed classification system is constructed based on the Gaussian mixture model-based clustering, the feature assignment algorithm, and the similarity measurement. Several features for each starch grain image are extracted and every class of drug is represented by a set of characteristic features. For each stage of the system, only one feature is chosen and assigned to that stage via the feature assignment algorithm, and the corresponding characteristic features are subdivided into smaller subsets based on clustering techniques. At the final stage, each subset contains a certain class of drugs (with corresponding characteristic features) and similarity measurement is carried out for starch grain classification. Three sets of the current state-of-the-art starch grain features including the granulometric size distribution, the chord length distribution, and the wavelet signature are used to construct the system. Experimental results on a database of 240 images of 24 classes of drugs reveal the superior performance of the multistage system. Comparison with the traditional starch grain classification approaches indicates that our proposed multistage method produces a marked improvement in classification performance.

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“…The general problem of such systems is their computational cost in data pre-preparation stage and projection into other spaces such as eigenspace [1,2], fisherspace [3,4], wavelet transform [5,6] and/or cosine transform [7]. On the other hand, many researchers have used statistical approaches such as the grey-level co-occurrence matrix [8] and its variants for the extraction of features in texture classification [9][10][11] and object recognition [12,13]. Local binary patterns algorithm is another powerful statistical algorithm that is widely used by researchers for image representation [14].…”

Section: Introductionmentioning

confidence: 99%

Simple and Novel Approach for Image Representation with Application to Face Recognition

Eleyan

2017

ijisae

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Abstract:In this paper, a new statistical image descriptor for the face recognition problem is proposed. To the best of our knowledge, no one has attempted to implement this approach before. The idea is simple and straight forward. For each face image, a feature descriptor is formed by concatenating 4 vectors together. These four vectors are formed by taking the sum of pixels in four different directions, namely; row sum (0 ), column sum (90 ), diagonal sum (45 ) and antidiagonal sum (−45 ). For test purposes, the generated feature descriptor is used in face recognition problem. The experiments are carried out on two different face databases namely; ORL and PUT databases. Simulation results show that the proposed approach gave a comparative performance to the well-known feature extraction algorithms in face recognition.

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Genetic Algorithm Optimization of Adaptive Multi-Scale GLCM Features

Cited by 41 publications

References 17 publications

A Personal Identification Framework based on Facial Image and Fingerprint Fusion Biometric

A Personal Identification Framework based on Facial Image and Fingerprint Fusion Biometric

A novel and effective multistage classification system for microscopic starch grain images

Simple and Novel Approach for Image Representation with Application to Face Recognition

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