Abstract Several convolutional neural network architectures have been proposed for handwritten character recognition. However, most of the conventional architectures demand large scale training data and long training time to obtain satisfactory results. These requirements prevent the use of these methods in a broader range of applications. As an alternative to cope with these problems, we present a new convolutional network for handwritten character recognition based on the Fukunaga–Koontz transform (FKT). Our approach lies in the assumption that Fukunaga–Koontz convolutional kernels can be efficiently learned from subspaces and directly employed to produce high discriminant features in a shallow network architecture. When representing image classes by subspaces, the within-class separability is reduced, since the subspaces form clusters in a low-dimensional space. To increase the between-class separability, we compute a discriminative space from the training subspaces using FKT. By learning convolutional kernels from subspaces, it is possible to extract representative and discriminative features from an image with only a few parameters. Another contribution of the proposed network is the use of pooling layers, which further improves its performance. The proposed method, called Fukunaga–Koontz Network (FKNet), is suitable for solving practical problems, especially when training and processing times are constraints. Four publicly available handwritten character datasets are employed to evaluate the advantages of FKNet. In addition, we demonstrate the flexibility of the proposed method by experiments on LFW dataset.

Automatic classification of bioacoustic signals is an essential tool in biology for laborious tasks such as environmental monitoring in areas of difficult access. A working system applied in the field must be able to run on small scale machines and make reasonable predictions from a small sample of data. Recently, a method called Grassmann singular spectrum analysis (GSSA) was introduced as the latest development in a line of research where bioacoustic signals are represented by subspaces. While this paradigm is compact and introduces a straightforward discriminant analysis for classification, it is based on a Grassmann kernel, which approximates the Grassmann manifold by a reproducing Hilbert kernel space, thus depending on a choice of a dictionary and not being able to capture the signals complexity from a small class sample. In this paper, we propose a method named tangent singular spectrum analysis (TSSA), which continues to exploit the advantages of subspace representation but does not rely on approximating the Grassmann manifold by a low-dimensional kernel. We formulate a discriminant analysis on a tangent space to the data sample mean, using the extrinsic coordinates of the manifold. The validity of TSSA is demonstrated through experiments on the Amazon rainforest Anuran dataset.

Bioacoustic signal classification is a powerful tool for biologists, assisting in tasks such as environmental monitoring of biomes in areas of difficult access, and providing clues about the evolution and categorization of animals from the perspective of similarity of their bioacoustic mechanisms. The recently proposed mutual singular spectrum analysis (MSSA) introduced a novel bioacoustic signal representation based on subspaces, which is compact and requires no cost intensive preprocessing techniques (e.g. segmentation, noise reduction or syllable extraction). However, MSSA has no discriminant mechanism to separate classes, and it assumes that a class is composed of linear combinations of the reference signals, which in practice is unlikely, and impairs study of the individuals' signals among the same species. In this paper we propose an extension named Grassmann singular spectrum analysis (GSSA), which preserves the advantages of MSSA in addition to the following contributions: we assume that a class may be composed of a set of subspaces and we simplify bioacoustic signal subspace representation by mapping the subspaces onto a Grassmann manifold; and we offer a discriminant mechanism to separate the species in a classification task. We demonstrate the validity of GSSA through a classification experiment on a publicly available bioacoustic signals dataset.

Text classification has become indispensable due to the rapid increase of text in digital form. Over the past three decades, efforts have been made to approach this task using various learning algorithms and statistical models based on bag-of-words (BOW) features. Despite its simple implementation, BOW features lack of semantic meaning representation. To solve this problem, neural networks started to be employed to learn word vectors, such as the word2vec. Word2vec embeds word semantic structure into vectors, where the angle between vectors indicates the meaningful similarity between words. To measure the similarity between texts, we propose the novel concept of word subspace, which can represent the intrinsic variability of features in a set of word vectors. Through this concept, it is possible to model text from word vectors while holding semantic information. To incorporate the word frequency directly in the subspace model, we further extend the word subspace to the term-frequency (TF) weighted word subspace. Based on these new concepts, text classification can be performed under the mutual subspace method (MSM) framework. The validity of our modeling is shown through experiments on the Reuters text database, comparing the results to various state-of-art algorithms.

In recent years, several convolutional neural networks-based architectures (CNN) have been proposed for handwritten character recognition. However, most of the conventional proposed models demand large scale training data and long training time to compute the parameters and achieve satisfactory results. These requirements prevent the use of these methods in a wider range of applications. To solve these problems, we present a novel convolutional network for handwritten character recognition based on subspace method. Our approach lies on the assumption that convolutional kernels can be efficiently learned from subspaces and directly employed to produce high discriminant features in a CNN architecture. When representing each image class by subspaces, we decrease the inter-class similarity, since the subspaces form clusters in a low dimensional space. In order to enlarge the intra-class similarity, we estimate a discriminative space from the training subspaces. By learning convolutional kernels from subspaces we can obtain representative and discriminative information produced by the network with few parameters, creating a light weight network. This use of a flexible architecture and its straightforward implementation make the proposed method quite attractive in practical terms. Our experimental evaluation shows that we achieve competitive results compared to the state-of-the-art methods.

Recently, convolutional neural network has attracted an increasing amount of attention in machine learning and computer vision areas, improving the performance of several related applications. Currently, many deep learning network architectures such as principal component analysis network (PCANet), linear discriminant analysis network (LDANet) and canonical correlation analysis network (CCANet) have been proposed for object and face classification. These architecture solutions have demonstrated high efficiency, with a simple implementation, providing a fast prototyping of efficient image classification applications. However, these solutions take advantage of filters that may not extract high discriminative features in more complicated computer vision problems (e.g. containing high degree of overlap between the distributions of the data). To generate more discriminative information, we introduce a discriminative canonical correlation network (DCCNet), that employs filters constructed from discriminative canonical correlations analysis (DCC). Learning filters from DCC ensures that the network will produce discriminative features, generating more representative and discriminative information. We demonstrate the applicability of DCCNet through experiments on four datasets.

Object recognition on large-scale video has recently attracted considerable research interest due to the huge amount of data available on the Internet, surveillance systems, social media networks and autonomous vehicles. By representing large-scale videos as image sets, we can handle the complex data variations such as viewpoint, illumination, and pose. In this paper, we propose an efficient and robust method for image set recognition based on Kernel Orthogonal Mutual Subspace Method (KOMSM), where sets of images are expressed as nonlinear subspaces. In our method, we formulate the image sets as nonlinear 2D subspaces by applying K2D-PCA and variants of 2D-PCA. Comparing to KOMSM, the proposed method requires less memory resource since it inherits the computational advantages of 2D-PCA and variants. In addition, the subspaces produced by K2D-PCA preserves the spatial relation between image pixels, generating more informative subspaces than KOMSM. The introduced method has the advantage of representing the subspaces in a more compact manner, achieving lower time complexity, confirming the suitability of employing 2D-PCA and variants. These results have been revealed through comprehensive experimentation conducted on five publicly available datasets.

The Amazon Rainforest degradation is a worldwide concern. The rainforest has been endangered by the illegal wood extraction without control even in the preservation areas. Due to the large geography extension prevent these crimes with an unmanned aerial vehicle (UAV) is not always possible. The Wireless Acoustics Sensor Network (WASNs) technology can alleviate this problem. Here, we present an acoustical framework to detect the sounds produced by several chainsaws. Our framework was developed to be embedded in a sensor node, combining the Mel-Fourier Cepstral Coefficients (MFCCs) with One-class classification technique. This classification method, that is based on a kernel density approach, allows us to recognize only chainsaw sounds rejecting all the other possible environmental sounds, such as: animal's calls, weather noises or boat engines. In the experiments, we varied the number MFCCs coefficients and the Kernel bandwidth performing a leave-one-out cross validation to find the best combination. Finally, we found that the best parameter combination achieve 98% of accuracy showing a low FNR and a high TPR, fact that enhances the credibility of the system avoiding false alarms and making it an optimal choice for an WSN application.