In recent object recognition research, the Sparse Representation based Classifier (SRC) and Collaborative Representation based Classification (CRC) have been widely used, achieving promising performances and robustness. However, both of these two algorithms are seldomly fused in classification based on the theory of probability. In this paper, we propose a novel image classification algorithm named Probabilistic Sparse-Collaborative Representation based Classifier (PSCRC), by fusing SRC and CRC. To boost the recognition performance and maintain the robustness of SRC, we introduce the theory of probability to offer different weights for each element in the coefficient vectors of SRC and CRC, respectively. We generate the probabilities of each sample in the training set by using Support Vector Machines (SVMs) which are fused with the coefficients of SRC and CRC. The proposed method is verified on five popular real word image datasets while being compared with other classifiers. The numerical results in the experiments show that the proposed classifier using our fusion strategy outperforms others.
Plant leaf species classification is an active research area at present with many scientists attempting to use different classifiers with different leaf features to solve it. In this paper we evaluate 10 common classifiers: k-Nearest Neighbors (KNN), support vector machine (SVM), nu-SVM, decision tree, random forest, naïve bayes, linear discriminant analysis (LDA), logistic regression, quadratic discriminant analysis (QDA) and sparse representation in leaf species classification with different leaf features such as shape, texture and margin. Besides this, different numbers of leaf species and training samples for different classifiers were also evaluated in this study. The comprehensive results indicate that random forest, followed by LDA, logistic regression and sparse representation are the most robust and accurate classifiers in leaf recognition using various features.
This paper presents an approach to the computer aided diagnosis (CAD) of diabetic retinopathy (DR) -- a common and
severe complication of long-term diabetes which damages the retina and cause blindness. Since red lesions are regarded
as the first signs of DR, there has been extensive research on effective detection and localization of these abnormalities
in retinal images. In contrast to existing algorithms, a new approach based on Multiscale Correlation Filtering (MSCF)
and dynamic thresholding is developed. This consists of two levels, Red Lesion Candidate Detection (coarse level) and
True Red Lesion Detection (fine level). The approach was evaluated using data from Retinopathy On-line Challenge
(ROC) competition website and we conclude our method to be effective and efficient.
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