Sequential learning is that discipline of machine learning that deals with dependent data. In this paper, we use the Multi-scale Stacked Sequential Learning approach (MSSL) to solve the task of pixel-wise classification based on contextual information. The main contribution of this work is a shifting technique applied during the testing phase that makes possible, thanks to template images, to c...

Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining in popularity due to the widespread deployment of low-cost sensors and their ...

In this work, a region-based Deep Convolutional Neural Network framework is proposed for document structure learning. The contribution of this work involves efficient training of region based classifiers and effective ensembling for document image classification. A primary level of ‘inter-domain’ transfer learning is used by exporting weights from a pre-trained VGG16 architecture on the ImageNe...

There are regional limitations in traditional methods of water body extraction. For different terrain, all the methods rely heavily on carefully hand-engineered feature selection and large amounts of prior knowledge. Due to the difficulty and high cost in acquiring, the labeled data of remote sensing is relatively small. Thus, there exist some challenges in the classification of huge amount of ...

Several studies on brain Magnetic Resonance Images (MRI) show relations between neuroanatomical abnormalities of brain structures and neurological disorders, such as Attention Defficit Hyperactivity Disorder (ADHD) and Alzheimer. These abnormalities seem to be correlated with the size and shape of these structures, and there is an active field of research trying to find accurate methods for aut...

In this paper, we present two supervised machine learning methods to automatically detect and recognize coral reef fishes in underwater HD videos. The first method relies on a traditional two-step approach: extraction of HOG features and use of a SVM classifier. The second method is based on Deep Learning. We compare the results of the two methods on real data and discuss their strengths and we...

Traditional way of conducting analyses of human behaviors is through manual observation. For example in couple therapy studies, human raters observe sessions of interaction between distressed couples and manually annotate the behaviors of each spouse using established coding manuals. Clinicians then analyze these annotated behaviors to understand the effectiveness of treatment that each couple ...

In the field of computer vision, CNNs are an evolving branch of deep learning algorithms that have attracted much attention as compared to the other deep learning methods. This is because of the intrinsic property of this group of networks that explicitly construct a hierarchical representation of input images, which result in a rich set of features for downstream classification tasks. CNNs gen...

In recent years, many deep architectures have been proposed in different fields. However, to obtain good results, most of the previous deep models need a large number of training data. In this paper, for small and middle scale applications, we propose a novel deep learning framework based on stacked feature learning models. Particularly, we stack marginal Fisher analysis (MFA) layer by layer fo...