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Image features can be learned and subsequently used for reconstruction and classification tasks in the fields of machine learning and computer vision. In this work, we propose image reconstruction using Convolutional Sparse Coding (CSC) on IBM's TrueNorth Neuromorphic computing system. CSC explicitly models local interactions through the convolution operations. Convolutional kernels define a dictionary...
Accurate identification of functionally relevant variants against the ubiquitous background genetic variations is a significant challenge facing bioinformatics researchers and the challenge becomes more severe for non-coding variants. In this study, a novel computational method to identify candidate disease-associated non-coding single nucleotide polymorphisms (SNPs) of human genome is presented....
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...
In recent years, SRC has received many attentions for classification and identification tasks. This paper attempts to introduce a sparse representation based classification of EEG signal features and identification of associated activities or tasks. It uses wavelet and ICA processing of EEG signal for feature selection and dictionary training. Multiple dictionaries are trained and used for EEG signal...
Biometrics represents the identity of individuals. Physical characteristics like voice, face, fingerprint, etc. are used to recognize individuals. Biometrics are used as a promising method for authentication, but use of these raw biometric data results in some privacy concerns. In this paper, we propose a system model for privacy preserving biometric authentication system for speech, face and fingerprint...
We address domain adaptation in the context of clustering where we are given a set of unlabeled data, coming from several domains, and the goal is to group data into different categories regardless of the domain they come from. This is a challenging problem since we do not have any supervision unlike most adaptation scenarios studied earlier, and is very relevant in practical industry applications...
SIFT-based identification techniques have been broadly criticised in biometrics due to its high false matching rate. To overcome this weakness, a new method for SIFT-based palmprint matching, called the Self Geometric Relationship-based matching (SGR-Matching) is presented. While existing matching techniques consider only the relationship between the SIFT-points of the query image on one hand and...
Image instance retrieval is the problem of retrieving images from a database which contain the same object. Convolutional Neural Network (CNN) based descriptors are becoming the dominant approach for generating global image descriptors for the instance retrieval problem. One major drawback of CNN-based global descriptors is that uncompressed deep neural network models require hundreds of megabytes...
Evaluation of coding efficiency is traditionally modeled as a continuous rate-distortion (R-D) function, where the peak signal-to-noise ratio (PSNR) is adopted as the quality measure. Although the PSNR-versus-bitrate curve offers some useful tradeoff information between video quality and coding bit-rates, it does not take human perceptual experience into account. In this work, by following the recent...
In this paper, we propose a new video representation incorporating image based deep features and an efficient pooling strategy for the purpose of action recognition. The Convolutional Neural Network (CNN) based features have very recently emerged as the new state of the art for image classification. Several attempts have been made to extend such CNN models for videos by explicitly focusing on the...
In this paper, we propose a robust descriptor named as multiple gradient-related features (MGRF) in virtue of local and overall order encoding. Specifically, three types of features are introduced, including multidirectional gradient, gradient orientation, and first derivative of gradient orientation, each of which represents different aspect of region of interest (ROI). To extract these features,...
Dense trajectories are widely used in human action recognition. However, the relationships among trajectories are rarely exploited and a large mount of useful information is missing. In this paper, we propose a novel approach to employ the space-time relationships between different trajectories for action recognition. In our approach, each trajectory is paired up with several neighbors which are spatially...
We have devised a method for estimating, from a single frame of audio frequency spectra, a shape parameter of multivariate generalized Gaussian distribution which has variance represented by an all-pole model and no covariance. Based on powered all-pole spectrum estimation (PAPSE), which is an extension of linear prediction, the proposed method simultaneously estimates the shape parameter and the...
Compared with H.264, High Efficient Video Coding (HEVC) improves the coding efficiency by 50% at the price of significant increase in encoding time, due to Rate Distortion Optimization (RDO) on large variations of block sizes and prediction modes. In this paper, a fast intra coding algorithm is proposed to alleviate the high computational complexity of HEVC intra-frame coding. The proposed algorithm...
In this research, we propose a particular version of KNN (K Nearest Neighbor) where the similarity between feature vectors is computed considering the similarity among attributes or features as well as one among values. The task of text summarization is viewed into the binary classification task where each paragraph or sentence is classified into the essence or non-essence, and in previous works,...
In this research, we propose the version of K Nearest Neighbor which considers similarity among attributes for computing the similarity between feature vectors. The text segmentation task is viewed into the binary classification where each pair of sentences or paragraphs is classified into whether we put the boundary or not, and the proposed version resulted in the successful results in previous works...
The Factored 3-way Restricted Boltzmann Machine has encoded the image transformation successfully. But when utilize the code to unknown image, the result was much affected by the feature of training samples. Based on the model, we separated the transformation feature out of the hidden representation and designed a new probabilistic model with gate for learning distributed representations of image...
In order to simulate this feature and detect the salient region rapidly, we propose the Spatial-Temporal Feature in Compress Domain (STFCD) model. By respectively using H.264 residual coding length and motion vector coding length, we simulate the salient stimulus intensity and then get video saliency features. Finally, we use the linear weighted fusion algorithm to get the final video saliency maps...
Content based indexing is critical to the effective access of the multimedia data. To this end, visual data is often annotated with textual content for bridging the semantic gap. In this paper, we present a method to generate frame level fine grained annotations for a given video clip. Access to the frame level fine grained annotations lead to rich, dense and meaningful semantic associations between...
Road detection from images is a challenging task in computer vision. Previous methods are not robust, because their features and classifiers cannot adapt to different circumstances. To overcome this problem, we propose to apply unsupervised feature learning for road detection. Specifically, we develop an improved encoding function and add a feature selection process to obtain robust and discriminative...
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