Decision Bayes Criteria for Optimal Classifier Based on Probabilistic Measures

This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions. Since there is a kind of equivalence between the probabilistic dependence measure and the Bayes classification error probability, we propose to use an iterative algorithm to optimize the dimension reduction for classification with a probabilistic approach to achieve the Bayes classifier. The estimated probabilities of different errors encountered along the different phases of the system are realized by the Kernel estimate which is adjusted in a means of the smoothing parameter. Experiment results suggest that the proposed approach performs well.

Robust Magnification Independent Colon Biopsy Grading System over Multiple Data Sources

Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification.This work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes:normal,well,moderate,and poor.The contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature,Gabor wavelet,wavelet moments,HSV histogram,color auto-correlogram,color moments,and morphological features that can be used to characterize different grades.Besides,the classifier is modeled as a multiclass structure with six binary class Bayesian optimized random forest(BO-RF)classifiers.This study uses four datasets(two collected from Indian hospitals—Ishita Pathology Center(IPC)of 4X,10X,and 40X and Aster Medcity(AMC)of 10X,20X,and 40X—two benchmark datasets—gland segmentation(GlaS)of 20X and IMEDIATREAT of 10X)comprising multiple microscope magnifications.Experimental results demonstrate that the proposed method outperforms the other methods used for colon cancer grading in terms of accuracy(97.25%-IPC,94.40%-AMC,97.58%-GlaS,99.16%-Imediatreat),sensitivity(0.9725-IPC,0.9440-AMC,0.9807-GlaS,0.9923-Imediatreat),specificity(0.9908-IPC,0.9813-AMC,0.9907-GlaS,0.9971-Imediatreat)and F-score(0.9725-IPC,0.9441-AMC,0.9780-GlaS,0.9923-Imediatreat).The generalizability of the model to any magnified input image is validated by training in one dataset and testing in another dataset,highlighting strong concordance in multiclass classification and evidencing its effective use in the first level of automatic biopsy grading and second opinion.

Face recognition system based on CNN and LBP features for classifier optimization and fusion

Face recognition has been a hot-topic in the field of pattern recognition where feature extraction and classification play an important role. However, convolutional neural network （CNN） and local binary pattern （LBP） can only extract single features of facial images, and fail to select the optimal classifier. To deal with the problem of classifier parameter optimization, two structures based on the support vector machine （SVM） optimized by artificial bee colony （ABC） algorithm are proposed to classify CNN and LBP features separately. In order to solve the single feature problem, a fusion system based on CNN and LBP features is proposed. The facial features can be better represented by extracting and fusing the global and local information of face images. We achieve the goal by fusing the outputs of feature classifiers. Explicit experimental results on Olivetti Research Laboratory （ORL） and face recognition technology （FERET） databases show the superiority of the proposed approaches.