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Image annotation is always an easy task for humans but a tough task for machines. Inspired by human's thinking mode, there is an assumption that the computer has double systems. Each of the systems can handle the task individually and in parallel. In this paper, we introduce a new hierarchical model for image annotation, based on constructing a novel, hierarchical tree, which consists of exploring...
In this paper, we address fine-grained classification which is quite challenging due to high intra-class variations and subtle inter-class variations. Most modern approaches to fine-grained recognition are established based on convolutional neural networks (CNN). Despite the effectiveness, these approaches still suffer from two major problems. First, they highly rely on large sets of training data,...
Several models based on deep neural networks have applied to single image super-resolution and obtained great improvements in terms of both reconstruction accuracy and computational performance. All these methods focus either on performing the super-resolution (SR) reconstruction operation in the high resolution (HR) space after upscaling with a single filter, usually bicubic interpolation, or optimizing...
This paper describes a novel algorithm to improve the performance of sparsity based single-channel speech separation(SCSS) problem based on compressed sensing which is an emerging technique for efficient data reconstruction. The conventional approach assumes the mixing conditions and source signals are stationary. For practical applications of audio source separation, however, we face the challenges...
In this paper, we propose a new single sample face recognition approach under the widely used sparse representation-based classification (SRC) framework. Previous work has shown that SRC only works well when there are sufficient number of training samples per person and not suitable for SSFR. To address this, we propose a domain transfer sparse representation-based classification (DT-SRC) method by...
We present VidedWhisfer, a novel approach for unsupervised video representation learning, in which video sequence is treated as a self-supervision entity based on the observation that the sequence encodes video temporal dynamics (e.g., object movement and event evolution). Specifically, for each video sequence, we use a pre-learned visual dictionary to generate a sequence of high-level semantics,...
Learning-based image super-resolution methods often use large datasets to learn texture features. When these methods are applied to depth images, emphasis should be given on learning the geometrical structures at object boundaries, since depth images do not have much texture information. In this paper, we develop a scheme to learn multiple residual dictionaries from only one external image. After...
A common practice for addressing the problem of verifying the presence, or the consent of a person in many transactions is to utilize the handwritten signature. Among others, the offline or static signature is a valuable tool in forensic related studies. Thus, the importance of verifying static handwritten signatures still poses a challenging task. Throughout the literature, gray-level images, composed...
This paper proposes a novel deep convolutional neural network (CNN), called sparse coding convolutional neural network (SC-CNN), to address the problem of sound event recognition and retrieval task. Unlike the general framework of a CNN, in which feature learning process is performed hierarchically, the proposed framework models the whole memorizing procedures in the human brain, including encoding,...
This paper considers single image super-resolution (SISR), which is an important low-level vision task and has various applications in multimedia society. Recently, deep neural networks have archived good performance on this field. But most of existing deep models are based on the fully data-dependent network architecture, thus missing majority of domain-knowledge of the super-resolution task. To...
Discriminative dictionary learning has been widely used in many applications such as face retrieval / recognition and image classification, where the labels of the training data are utilized to improve the discriminative power of the learned dictionary. This paper deals with a new problem of learning a dictionary for associating pairs of images in applications such as face image retrieval. Compared...
In this paper, balanced two-stage residual networks (BTSRN) are proposed for single image super-resolution. The deep residual design with constrained depth achieves the optimal balance between the accuracy and the speed for super-resolving images. The experiments show that the balanced two-stage structure, together with our lightweight two-layer PConv residual block design, achieves very promising...
This paper presents the work done towards developing a speech corpus for Romanian, for automatic speech recognition for the banking domain. This work is done in the context of the Speech2Process project, which aims at creating a system which allows interaction between customers and agents in the contact center much easier. The application to use the banking corpus will provide automatic response to...
This paper introduces a novel open access resource, the machine-readable phonetic dictionary for Romanian — MaRePhoR. It contains over 70,000 word entries, and their manually performed phonetic transcription. The paper describes the dictionary format and statistics, as well as an initial use of the phonetic transcription entries by building a grapheme to phoneme converter based on decision trees....
With recent advances in mobile computing and sensing technology, smart wearable devices have pervaded our everyday lives. The security of these wearable devices is becoming a hot research topic because they store various private information. Existing approaches either only rely on a secret PIN number or require an explicit user authentication process. In this paper, we present Gait-watch, a context-aware...
This work investigates a statistical technique for high performance remote-sensing imagery compression. By exploiting existing remote-sensing data sets, useful structural and texture prior information can be learned. The main methodologies are Bayesian dictionary learning and stochastic approximation. A Bayesian network simulating the generation mechanism of remote- sensing images is modelled. The...
In this study, a vision based in-car entertainment user interface is presented. The user interface is designed using a hand posture and gesture recognition algorithm in deep learning framework. The hand posture recognition algorithm is formulated using the convolutional neural network to perform the fundamental tasks in the user interface. The hand gesture recognition algorithm is formulated using...
Person re-identification is known as matching an individual captured in one or more cameras using a gallery of provided candidates from a different camera view. It is a hard task owing to variations in illumination, viewpoints, poses and small number of annotated training individuals. For obtaining the proper distance metrics, we propose a novel approach based on dictionary learning. Our method decomposes...
As the main body of modern traffic, transport vehicle is the focus of intelligent transportation systems. For three typical vehicles (including the automobile, motorcycle and bicycle), this paper proposes a new transport vehicle recognition system via class dictionary learning. For solving problems in the traditional transport vehicle recognition under sparse recognition framework, our method use...
In most of the existing regression-based methods, mapping matrices are directly learnt from features which are extracted from the interpolation results of low-resolution (LR) images. Nevertheless, this kind of features usually suffer from many artifacts which may produce bad effects on image super-resolution (SR) reconstruction. In this paper, we propose an effective single image super-resolution...
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