Deep semantic segmentation is a task that identifies objects and their boundaries in images, to do that a classification task is performed at the pixel level to tag whether a pixel belongs to an object.In skin detection, areas of images are classified as skin or non-skin regions.In this work, we report a short survey of the recent literature covering the task to help researchers in selecting the most suitable method for their application and to expand the knowledge about the available datasets for this topic.A compact empirical evaluation comparing recent models and a new ensemble model is reported.

Abstract A problem with convolutional neural networks (CNNs) is that they require large datasets to obtain adequate robustness; on small datasets, they are prone to overfitting. Many methods have been proposed to overcome this shortcoming with CNNs. In cases where additional samples cannot easily be collected, a common approach is to generate more data points from existing data using an augmentation technique. In image classification, many augmentation approaches utilize simple image manipulation algorithms. In this work, we propose some new methods for data augmentation based on several image transformations: the Fourier transform (FT), the Radon transform (RT), and the discrete cosine transform (DCT). These and other data augmentation methods are considered in order to quantify their effectiveness in creating ensembles of neural networks. The novelty of this research is to consider different strategies for data augmentation to generate training sets from which to train several classifiers which are combined into an ensemble. Specifically, the idea is to create an ensemble based on a kind of bagging of the training set, where each model is trained on a different training set obtained by augmenting the original training set with different approaches. We build ensembles on the data level by adding images generated by combining fourteen augmentation approaches, with three based on FT, RT, and DCT, proposed here for the first time. Pretrained ResNet50 networks are finetuned on training sets that include images derived from each augmentation method. These networks and several fusions are evaluated and compared across eleven benchmarks. Results show that building ensembles on the data level by combining different data augmentation methods produce classifiers that not only compete competitively against the state-of-the-art but often surpass the best approaches reported in the literature.

MOTIVATION: Because DNA-binding proteins (DNA-BPs) play a vital role in all aspects of genetic activity, the development of reliable and efficient systems for automatic DNA-BP classification is becoming a crucial proteomic technology. Key to this technology is the discovery of powerful protein representations and feature extraction methods. The goal of this article is to develop experimentally a system for automatic DNA-BP classification by comparing and combining different descriptors taken from different types of protein representations. RESULTS: The descriptors we evaluate include those starting from the position-specific scoring matrix (PSSM) of proteins, those derived from the amino-acid sequence (AAS), various matrix representations of proteins and features taken from the three-dimensional tertiary structure of proteins. We also introduce some new variants of protein descriptors. Each descriptor is used to train a separate support vector machine (SVM), and results are combined by sum rule. Our final system obtains state-or-the-art results on three benchmark DNA-BP datasets. AVAILABILITY AND IMPLEMENTATION: The MATLAB code for replicating the experiments presented in this paper is available at https://github.com/LorisNanni.

Bioimage classification is increasingly becoming more important in many biological studies including those that require accurate cell phenotype recognition, subcellular localization, and histopathological classification. In this paper, we present a new General Purpose (GenP) bioimage classification method that can be applied to a large range of classification problems. The GenP system we propose is an ensemble that combines multiple texture features (both handcrafted and learned descriptors) for superior and generalizable discriminative power. Our ensemble obtains a boosting of performance by combining local features, dense sampling features, and deep learning features. Each descriptor is used to train a different Support Vector Machine that is then combined by sum rule. We evaluate our method on a diverse set of bioimage classification tasks each represented by a benchmark database, including some of those available in the IICBU 2008 database. Each bioimage classification task represents a typical subcellular, cellular, and tissue level classification problem. Our evaluation on these datasets demonstrates that the proposed GenP bioimage ensemble obtains state-of-the-art performance without any ad-hoc dataset tuning of the parameters (thereby avoiding any risk of overfitting/overtraining). To reproduce the experiments reported in this paper, the MATLAB code of all the descriptors is available at https://github.com/LorisNanni and https://www.dropbox.com/s/bguw035yrqz0pwp/ElencoCode.docx?dl=0.

Image identification of animals is mostly centred on identifying them based on their appearance, but there are other ways images can be used to identify animals, including by representing the sounds they make with images. In this study, the authors present a novel and effective approach for automated identification of birds and whales using some of the best texture descriptors in the computer vision literature. The visual features of sounds are built starting from the audio file and are taken from images constructed from different spectrograms and from harmonic and percussion images. These images are divided into sub‐windows from which sets of texture descriptors are extracted. The experiments reported in this study using a dataset of Bird vocalisations targeted for species recognition and a dataset of right whale calls targeted for whale detection (as well as three well‐known benchmarks for music genre classification) demonstrate that the fusion of different texture features enhances performance. The experiments also demonstrate that the fusion of different texture features with audio features is not only comparable with existing audio signal approaches but also statistically improves some of the stand‐alone audio features. The code for the experiments will be publicly available at https://www.dropbox.com/s/bguw035yrqz0pwp/ElencoCode.docx?dl=0 .

MOTIVATION: Given an unknown compound, is it possible to predict its Anatomical Therapeutic Chemical class/classes? This is a challenging yet important problem since such a prediction could be used to deduce not only a compound's possible active ingredients but also its therapeutic, pharmacological and chemical properties, thereby substantially expediting the pace of drug development. The problem is challenging because some drugs and compounds belong to two or more ATC classes, making machine learning extremely difficult. RESULTS: In this article a multi-label classifier system is proposed that incorporates information about a compound's chemical-chemical interaction and its structural and fingerprint similarities to other compounds belonging to the different ATC classes. The proposed system reshapes a 1D feature vector to obtain a 2D matrix representation of the compound. This matrix is then described by a histogram of gradients that is fed into a Multi-Label Learning with Label-Specific Features classifier. Rigorous cross-validations demonstrate the superior prediction quality of this method compared with other state-of-the-art approaches developed for this problem, a superiority that is reflected particularly in the absolute true rate, the most important and harshest metric for assessing multi-label systems. AVAILABILITY AND IMPLEMENTATION: The MATLAB code for replicating the experiments presented in this article is available at https://www.dropbox.com/s/7v1mey48tl9bfgz/ToolPaperATC.rar?dl=0 . CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper a novel approach for automatic bird species classification is described. The proposed strategy is based on features taken from the textural content of spectrogram images of bird vocalizations. We show how several texture descriptors can be used for representing the spectrograms. The following approaches are tested here with spectrograms for the first time: Local Ternary Phase Quantization, Heterogeneous Auto-Similarities of Characteristics, and an ensemble of variants of Local Binary Pattern Histogram Fourier. Combining this set of descriptors greatly increases classification performance and markedly improves previous ensembles of texture descriptors used for describing a spectrogram. Moreover, a further improvement is obtained when the texture descriptors are combined with the acoustic features. SVM classifiers are used in the classification step, with final results computed using 10-fold cross-validation. For a fair comparison with other methods in the literature, the experiments are performed on a benchmark database composed of 46 bird species used for this classification task. The best accuracy rate obtained is about 94.5%. The MATLAB code we used is publicly available to other researchers for future comparisons, as well as the database used in the experiments.

MOTIVATION: The microarray report measures the expressions of tens of thousands of genes, producing a feature vector that is high in dimensionality and that contains much irrelevant information. This dimensionality degrades classification performance. Moreover, datasets typically contain few samples for training, leading to the 'curse of dimensionality' problem. It is essential, therefore, to find good methods for reducing the size of the feature set. RESULTS: In this article, we propose a method for gene microarray classification that combines different feature reduction approaches for improving classification performance. Using a support vector machine (SVM) as our classifier, we examine an SVM trained using a set of selected genes; an SVM trained using the feature set obtained by Neighborhood Preserving Embedding feature transform; a set of SVMs trained using a set of orthogonal wavelet coefficients of different wavelet mothers; a set of SVMs trained using texture descriptors extracted from the microarray, considering it as an image; and an ensemble that combines the best feature extraction methods listed above. The positive results reported offer confirmation that combining different features extraction methods greatly enhances system performance. The experiments were performed using several different datasets, and our results [expressed as both accuracy and area under the receiver operating characteristic (ROC) curve] show the goodness of the proposed approach with respect to the state of the art. AVAILABILITY: The MATHLAB code of the proposed approach is publicly available at bias.csr.unibo.it/nanni/micro.rar.

The availability of a reliable prediction method for prediction of bacterial virulent proteins has several important applications in research efforts targeted aimed at finding novel drug targets, vaccine candidates, and understanding virulence mechanisms in pathogens. In this work, we have studied several feature extraction approaches for representing proteins and propose a novel bacterial virulent protein prediction method, based on an ensemble of classifiers where the features are extracted directly from the amino acid sequence and from the evolutionary information of a given protein. We have evaluated and compared several ensembles obtained by combining six feature extraction methods and several classification approaches based on two general purpose classifiers (i.e., Support Vector Machine and a variant of input decimated ensemble) and their random subspace version. An extensive evaluation was performed according to a blind testing protocol, where the parameters of the system are optimized using the training set and the system is validated in three different independent data sets, allowing selection of the most performing system and demonstrating the validity of the proposed method. Based on the results obtained using the blind test protocol, it is interesting to note that even if in each independent data set the most performing stand-alone method is not always the same, the fusion of different methods enhances prediction efficiency in all the tested independent data sets.

No abstract available.

BACKGROUND: In this paper, it is proposed an optimization approach for producing reduced alphabets for peptide classification, using a Genetic Algorithm. The classification task is performed by a multi-classifier system where each classifier (Linear or Radial Basis function Support Vector Machines) is trained using features extracted by different reduced alphabets. Each alphabet is constructed by a Genetic Algorithm whose objective function is the maximization of the area under the ROC-curve obtained in several classification problems. RESULTS: The new approach has been tested in three peptide classification problems: HIV-protease, recognition of T-cell epitopes and prediction of peptides that bind human leukocyte antigens. The tests demonstrate that the idea of training a pool classifiers by reduced alphabets, created using a Genetic Algorithm, allows an improvement over other state-of-the-art feature extraction methods. CONCLUSION: The validity of the novel strategy for creating reduced alphabets is demonstrated by the performance improvement obtained by the proposed approach with respect to other reduced alphabets-based methods in the tested problems.

In this paper, a new approach for pedestrian detection is presented. We design an ensemble of classifiers that employ different feature representation schemes of the pedestrian images: Laplacian EigenMaps, Gabor filters, and invariant local binary patterns. Each ensemble is obtained by varying the patterns used to train the classifiers and extracting from each image two feature vectors for each feature extraction method: one for the upper part of the image and one for the lower part of the image. A different radial basis function support vector machine (SVM) classifier is trained using each feature vector; finally, these classifiers are combined by the ldquosum rule.rdquo Experiments are performed on a large data set consisting of 4000 pedestrian and more than 25 000 nonpedestrian images captured in outdoor urban environments. Experimental results confirm that the different feature representations give complementary information, which has been exploited by fusion rules, and we have shown that our method outperforms the state-of-the-art approaches among pedestrian detectors.

The problem addressed in this letter concerns the multiclassifier generation by a random subspace method (RSM). In the RSM, the classifiers are constructed in random subspaces of the data feature space. In this letter, we propose an evolved feature weighting approach: in each subspace, the features are multiplied by a weight factor for minimizing the error rate in the training set. An efficient method based on particle swarm optimization (PSO) is here proposed for finding a set of weights for each feature in each subspace. The performance improvement with respect to the state-of-the-art approaches is validated through experiments with several benchmark data sets.

Prediction of protein-protein interaction is a difficult and important problem in biology. In this paper, we propose a new method based on an ensemble of K-local hyperplane distance nearest neighbor (HKNN) classifiers, where each HKNN is trained using a different physicochemical property of the amino acids. Moreover, we propose a new encoding technique that combines the amino acid indices together with the 2-Grams amino acid composition. A fusion of HKNN classifiers combined with the 'Sum rule' enables us to obtain an improvement over other state-of-the-art methods. The approach is demonstrated by building a learning system based on experimentally validated protein-protein interactions in human gastric bacterium Helicobacter pylori and in Human dataset.