Content based image retrieval using hybrid features and various distance metric

Content-based image retrieval approach for biometric security using colour, texture and shape features controlled by fuzzy heuristics

Quaternion polar complex exponential transform for invariant color image description

A novel support vector machine (SVM) model combining kernel principal component analysis (KPCA) with genetic algorithm (GA) is proposed for intrusion detection. In the proposed model, a multi-layer SVM classifier is adopted to estimate whether the action is an attack, KPCA is used as a preprocessor of SVM to reduce the dimension of feature vectors and shorten training time. In order to reduce the noise caused by feature differences and improve the performance of SVM, an improved kernel function (N-RBF) is proposed by embedding the mean value and the mean square difference values of feature attributes in RBF kernel function. GA is employed to optimize the punishment factor C, kernel parameters @s and the tube size @? of SVM. By comparison with other detection algorithms, the experimental results show that the proposed model performs higher predictive accuracy, faster convergence speed and better generalization.

A Novel Hybrid Kpca and SVM with ga Model for Intrusion Detection

Extraction of the breast border and simultaneously exclusion of pectoral muscle are principal steps for diagnosing of breast cancer based on mammogram data. The objective of propose method is to classify the mediolateral oblique fragment of the pectoral muscle. The extraction of breast region is performed using the multilevel wavelet decomposition of mammogram images. Moreover, artifact suppression and pectoral muscle detection is carried out by morphological operator. The efficient extraction with higher accuracy is validated on the set of 322 digital images taken from MIAS dataset.

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Extraction of breast border and removal of pectoral muscle in wavelet domain

A multiresolution approach for rotation invariant texture image retrieval with orthogonal polynomials model

Image retrieval using edge based shape similarity with multiresolution enhanced orthogonal polynomials model

Taguchi approach and decision tree algorithm for prediction of wear rate in zinc oxide-filled AA7075 matrix composites

Computer Aided Diagnosis systems (CAD) are the recent trend in digital mammography which helps radiologist in detection of breast cancer Recent studies are showing that mammography density is strongly associated with breast cancer and it is one of the important risk indicators of this disease Automatic detection of breast lesion is depends on effective pre-processing and breast tissue type classification Hence this paper proposes a new method for mammogram image pre-processing and tissue type classification In pre-processing automatic removal of noises, labels and artifacts is performed by morphological operations followed by connected component labelling and pectoral muscle is eliminated by polynomial curve fitting method Statistical features like Mean, Standard deviation, Smoothness, Skewness, Uniformity, Kurtosis, Histogram and correlation are the important texture features of the breast tissue are extracted and fed into Support Vector Machine (SVM) for classification Our method is experimented on Mini-MIAS database (Mammographic Image Analysis Society, London, UK) for classification of mammogram into any of the three classes namely Fatty, Dense and Glandular and it yields good result when compared with existing techniques

S. Sathiya Devi

Papers

A multiresolution approach for rotation invariant texture image retrieval with orthogonal polynomials model

Image retrieval using edge based shape similarity with multiresolution enhanced orthogonal polynomials model

Taguchi approach and decision tree algorithm for prediction of wear rate in zinc oxide-filled AA7075 matrix composites

Classification of breast tissue density in digital mammograms

A simple computational model for image retrieval with weighted multifeatures based on orthogonal polynomials and genetic algorithm