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Diabetic retinopathy (DR) is a complication of prolonged diabetic mellitus causing damage to the micro-vascular system in retina. Computer-aided detection of DR can significantly reduce time, effort, and cost in early diagnosis and thereby restrict further complications. In this paper, we present a deep learning based framework for automated DR stage classification from fundus retinal images.We design and develop a custom build Convolutional Neural Network (CNN) model named Model-IVS to extract meaningful and discriminative features from the retinal fundus images. The proposed hybrid-CNN combines the multi-scale pyramidal feature processing concept of inception model with the convolutional pipeline based feature extraction concept of VGG-Nets and adopts multi-scale feature concatenation based short-cut connections of DenseNet with learnable convolutions. For the binary classification tasks, we employ Model-IVS as a feature extractor and use Support Vector Machine (SVM) classifier on the reduced features obtained through principal component analysis. The proposed hybrid model is end-to-end trained incrementally on a class-balanced dataset curated from the train set of EyePACS dataset. The cross-dataset generalization of the proposed hybrid CNN based DR classification framework is evaluated on APTOS, MESSIDOR-1 and MESSIDOR-2 datasets. Experimental results demonstrate that our model has outperformed all the existing state-of-the-art methods for DR severity grading task on APTOS dataset. The proposed Model-IVS has achieved best accuracy, AUC-ROC, and kappa score of 84.33%, 0.978 and 0.899, respectively in DR severity grading task. In binary classification tasks like DR screening and referable DR predictions, our Model-IVS feature extraction network with SVM classifier on PCA features has also achieved comparable performance results w.r.t. the state-of-the-art methods. In DR screening task, we recorded an accuracy of 97.84%, 87.42%, and 89.67% on APTOS, MESSIDOR-1, and MESSIDOR-2 datasets, respectively. In referable DR prediction, we achieved AUC-ROC score of 0.987, 0.928, and 0.873 on APTOS, MESSIDOR-1, and MESSIDOR-2 datasets, respectively. [ABSTRACT FROM AUTHOR]

Facial paralysis (FP) is the most common illness. Nerve damage can cause the affected muscles of the face to lose control. Most FP diagnosis systems heavily depend on skilled clinicians and lack automatic quantitative assessment. This paper introduces a novel automatic facial paralysis recognition (autoFPR) approach, a four-stage machine learning solution, for classifying FP and healthy subjects. Our solution includes the creation of the dataset, feature extraction, dimensionality reduction, and classification. The FP and healthy subject videos are collected from the publicly available YouTube Facial Palsy database and the 300VW database, respectively. Facial features are extracted from the collected facial videos using landmarks, action units, eye gaze, and head pose. Five different experiments were performed to find the best facial paralysis recognition technique. autoFPR can distinguish subjects with facial paralysis and healthy individuals by using movement changes in their eyebrows, eyes, and corners of the mouth. Our goal is to prove that machine learning classifiers are capable of diagnosing FP. We evaluate system performance using accuracy, precision, recall, and F1. Feature selection plus principal component analysis with support vector machine achieved the highest accuracy, with 97.3% for the extracted features. [ABSTRACT FROM AUTHOR]

Many investigations have proved that the acoustics method is intuitive and effective for determining watermelon ripeness. The objective of this work is to drive a new robust acoustics classification scheme KPCA-ELM, which is based on the kernel principal component analysis (KPCA) and extreme learning machine (ELM). Acoustic signals are sampled by a microphone from unripe, ripe and over-ripe watermelon samples, which are randomly divided into two sample sets for training and testing. A set of basic signals is first obtained via KPCA of the training sample. Thus, any given signal can be represented as a linear combination of basis signals, and the coefficients of linear combination are extracted as the features of a signal. Corresponding to the unripe, ripe and over-ripe watermelons, a three-class ELM identification model is constructed based on the training data. The scheme presented in this paper is tested with the testing sample and an accuracy of 92% is achieved. To further evaluate the scheme performance, a comparison of ELM and SVM is conducted in terms of the classification results. The results reveal that the proposed scheme can classify faster than SVM, while ELM is better than SVM in accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a semantic concept method to recognize multi-ethnic people based on Axiomatic fuzzy set (AFS) theory with application to image analysis. There are two advantages of the proposed approach: (i) It can convert the facial features to semantic concepts and in such a way we bridge the semantic gap between low level pixel features and interpretable concepts. (ii) It can implement the logical operation of semantic concepts in the AFS framework. Technically, we first construct facial features utilizing the facial landmarks such as eyes, nose, mouth, and face contour. Second, we establish some corresponding semantic concepts to describe facial features. Finally, a set of the semantic concept rules are extracted to form a classifier aimed at identifying facial ethnic attributes. The efficacy of the proposed approach is verified on Chinese Multi-ethnic face database (CMFD), FEI and CK. Meanwhile, we first demonstrate that the selected features have two obvious advantages: (1) these features can achieve better performance for ethical recognition than the features based on pixel values directly. (2) The selected features can be obtained via facial landmark detector regardless of the image resolutions. Then, we compare the proposed approach with some existing classifiers using the selected features, such as principal component analysis (PCA), C4.5, Decision table, Cart, Fuzzy Decision Tree (FDT) and Repeated Incremental Pruning to Produce Error Reduction (Ripper), extensive experiments show that our method exhibits a similar performance with these methods, which is demonstrated by Friedman test, however, our proposed approach can provide interpretability and comprehension capability. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach of Gabor feature based on bi-directional two-dimensional principal component analysis ((2D)2PCA) for somatic cells recognition. Firstly, Gabor features of different orientations and scales are extracted by the convolution of Gabor filter bank. Secondly, dimensionality reduction of the feature space applies (2D)2PCA in both row and column. Finally, the classifier uses Support Vector Machine (SVM) to achieve our goal. The experimental results are obtained using a large set of images from different sources. The results of our proposed method are not only efficient in accuracy and speed, but also robust to illumination in bovine mastitis via optical microscopy. [ABSTRACT FROM AUTHOR]

Several 2DPCA-based face recognition algorithms have been proposed hoping to achieve the goal of improving recognition rate while mostly at the expense of computation cost. In this paper, an approach named SI2DPCA is proposed to not only reduce the computation cost but also increase recognition performance at the same time. The approach divides a whole face image into smaller sub-images to increase the weight of features for better feature extraction. Meanwhile, the computation cost that mainly comes from the heavy and complicated operations against matrices is reduced due to the smaller size of sub-images. The reduced amount of computation has been analyzed and the integrity of sub-images has been discussed thoroughly in the paper. The experiments have been conducted to make comparisons among several better-known approaches and SI2DPCA. The experimental results have demonstrated that the proposed approach works well on reaching the goals of reducing computation cost and improving recognition performance simultaneously. [ABSTRACT FROM AUTHOR]

This paper proposes hybrid approaches based on both feature level and score level fusion strategies to provide a robust recognition system against the distortions of individual modalities. In order to compare the proposed schemes, a virtual multimodal database is formed from FERET face and PolyU palmprint databases. The proposed hybrid systems concatenate features extracted by local and global feature extraction methods such as Local Binary Patterns, Log Gabor, Principal Component Analysis and Linear Discriminant Analysis. Match score level fusion is performed in order to show the effectiveness and accuracy of the proposed schemes. The experimental results based on these databases reported a significant improvement of the proposed schemes compared with unimodal systems and other multimodal face-palmprint fusion methods. [ABSTRACT FROM AUTHOR]

Vehicle type/make recognition based on images captured by surveillance cameras is a challenging task in intelligent transportation system and automatic surveillance. In this paper, we comparatively studied two feature extraction methods for image description, i.e. a new multiresolution analysis tool called Fast Discrete Curvelet Transform and the pyramid histogram of oriented gradients (PHOG). Curvelet Transform has better directional and edge representation abilities than widely used wavelet transform, which is particularly appropriate for the description of images rich with edges. PHOG represents the local shape by a histogram of edge orientations computed for each image sub-region, quantized into a number of bins, thus has the ascendency in its description of more discriminating information. A composite feature description from PHOG and Curvelet can further increase the accuracy of classification by taking their complementary information. We also investigated the applicability of the Rotation Forest (RF) ensemble method for vehicle classification based on the combined features. The RF ensemble contains a set of base multilayer perceptrons which are trained using principal component analysis to rotate the original axes of combined features of vehicle images. The class label is assigned by the ensemble via majority voting. Experimental results using more than 600 images from 21 makes of cars/vans show the effectiveness of the proposed approach. The composite feature is better than any single feature in the classification accuracy and the ensemble model produces better performance compared to any of the individual neural network base classifier. With a moderate ensemble size of 20, the Rotation Forest ensembles offers a classification rate close to 96.5%, exhibiting promising potentials for real-life applications. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid feature extraction technique using 2D principal component analysis (2DPCA) and discrete orthogonal Krawtchouk moment (KM) are used to extract the global and local features from the face. Ensemble of RBF classifiers are used to classify the image. Decision-level fusion is done using fuzzy integral to generate more accurate classification than each of the constituent classifiers. The proposed system is evaluated using ORL and YALE databases. Experimental results show that the combination of global and local features promotes the system performance. The fusion of multiple RBFs using fuzzy integral performed better as compared to conventional aggregation rules. [ABSTRACT FROM AUTHOR]

With the increasing demand of visual surveillance systems, human recognition at a distance has gained extensive research interest. Gait is a potential behavioral feature to identify humans based on their motion. This paper describes a new scheme for extracting and selecting features from the gait of a human for recognition. The scheme combines both Key Fourier Descriptors (KFDs) and principal component analysis (PCA) techniques. This leads to a strength in reducing feature space by KFD, and increasing accuracy by PCA. Also, it is shown that the proposed scheme leads to a higher correct classification rate than schemes that depend on KFD alone or PCA alone. [ABSTRACT FROM AUTHOR]