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Most existing hashing methods resort to binary codes for similarity search, owing to the high efficiency of computation and storage. However, binary codes lack enough capability in similarity preservation, resulting in less desirable performance. To address this issue, we propose an asymmetric multi-valued hashing method supported by two different non-binary embeddings. (1) A real-valued embedding...
The primary objective of this paper is to explore the applicability of sparse representation based classification (SRC), particularly at the fingerprint recognition problem. This paper proposes sparse proximity based fingerprint matching methodology. The sparse representation based classification problem can be solved as representing the test sample in terms of training set with some sparse residual...
The development of a normalized morpho-syntactic Arabic lexicon is not an easy task. In fact, many norms allow the structuration and representation of lexical data. The adoption of a stable standard will guarantee the interoperability and interchangeability of lexical resources. Still, research work that deals with normalization for Arabic lexical resources is not well developed yet, especially for...
In this paper, we propose a supervised dictionary learning algorithm that aims to preserve the local geometry in both dimensions of the data. A graph-based regularization explicitly takes into account the local manifold structure of the observations. A second graph regularization gives similar treatment to the feature domain and helps in learning a more robust dictionary. Both graphs can be constructed...
In this paper, three new algorithms are presented by applying group idea and collaborative thought to projective dictionary pair learning (DPL). These algorithms further extend the framework of discriminative dictionary learning (DL). Based on projective dictionary pair learning which realizes the goals of signal representation and pattern classification by learning a synthesis dictionary and an analysis...
Previous dictionary learning algorithms usually take the locality information of training samples into account in the learning process, and it may degrade the robustness of the dictionary. In this paper, an new locality constrained dictionary learning algorithm (LCDL) for face recognition by using the locality characters of atoms is proposed. Since the atoms are learned from the training samples,...
In this paper, we present a novel scheme for text-independent online writer identification. As a first contribution, we propose histogram based features, inspired from the area of object detection, to describe the structural primitives of handwriting. Secondly, we have used sparse coding techniques to learn prototypes, that describe the general writing characteristics of the authors. To the best of...
In this paper, a sub-dictionary based sparse coding method is proposed for image representation. The novel sparse coding method substitutes a new regularization item for L1-norm in the sparse representation model. The proposed sparse coding method involves a series of sub-dictionaries. Each sub-dictionary contains all the training samples except for those from one particular category. For the test...
Short Message Service (SMS) via cell phones is a widely used mode of data communication. Currently employed encoding schemes allow the transmission of 160 characters per SMS in English. This drops to 70 characters per SMS if any Indian language including Hindi is used, due to the UNICODE format used therein. Schemes proposed to improve the encoding efficiency of short text messaging generally encode...
Recently sparse and collaborative representation based classification has been developed for face recognition with single sample per person (SSPP). By using variations extracted from a generic training set as an additional common dictionary, promising performance has been reported in face recognition with SSPP. However, existing representation based classifiers for face recognition with SSPP ignored...
Behavior recognition from large available motion capture data has received wide attention in the computer animation community and is growing increasingly important in recent years. In this paper, we present an efficient motion capture behavior recognition approach via neighborhood preserving dictionary learning. First, we normalize all the motion sequences in the database to make the motion to be...
In this work we propose a novel approach for RDF (Resource Description Framework) dictionary encoding that employs a parallel RDF parser and a distributed dictionary data structure, exploiting RDF-specific optimizations. In contrast with previous solutions, this approach exploits the Partitioned Global Address Space (PGAS) programming model combined with active messages. We evaluate the performance...
To store a acquired ECG data huge space is required even if the ECG signal is sampled at lower rate. The relevance of telecardiology is more pronounced in contemporary times for diagnosing heart abnormalities by analyzing ECG signal. Transmission of such huge data for medical purpose is not possible without compression and also it will required more energy for wireless transmission for large amount...
We present a locality preserving K-SVD (LP-KSVD) algorithm for joint dictionary and classifier learning, and further incorporate kernel into our framework. In LP-KSVD, we construct a locality preserving term based on the relations between input samples and dictionary atoms, and introduce the locality via nearest neighborhood to enforce the locality of representation. Motivated by the fact that locality-related...
Dictionary learning has been applied to computer vision problems such as facial expression recognition. K-SVD is one of the state-of-the-art dictionary learning algorithms. However, K-SVD is unsupervised and focuses only on the representational power. In this paper, we adopt label-consistent K-SVD with scattering transform in facial expression recognition. In addition to reducing the reconstruction...
Deep brain stimulation (DBS) of Subthalamic Nucleus (STN) is the best method for treating advanced Parkinson's disease (PD), leading to striking improvements in motor function and quality of life of PD patients. During DBS, online analysis of microelectrode recording (MER) signals is a powerful tool to locate the STN. Therapeutic outcomes depend of a precise positioning of a stimulator device in the...
The ECG digital signal processing evolved in last decade and the need to store and transmit ECG signal data is continuously increasing. This paper involves analysis of an enhanced ECG compression and de-compression method. The method is evaluated on the basis of different compression and quality parameters like compression ratio (CR), percent root mean square difference (PRD), Signal to noise ratio...
A complex analytic flow in a modern enterprise may perform multiple, logically independent, tasks where each task uses a different processing engine. We term these multi-engine flows hybrid flows. Using multiple processing engines has advantages such as rapid deployment, better performance, lower cost, and so on. However, as the number and variety of these engines grows, developing and maintaining...
To search for a particular motion from a large database, a user-friendly and efficient retrieval mechanism is essential. In this paper, we propose a human motion retrieval system based on sparse coding and touch less interactions. Compared with existing methods that involve vector quantization, sparse coding leads to a more compact and discriminative representation. Motion comparison based on sparse...
Currently, there are no online websites that can provide mutual translation between Chinese and Tibetan. This paper introduces the online dictionary's functions and the technical route. And then this paper proposes the basic method to build Tibetan-Chinese dictionary which is based on Tibetan semantic ontology. Central to this approach is to build Tibetan semantic ontology with some semantic relationship...
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