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Iris recognition is a biometric authentication system proving vital for ensuring security and has been employed as an important case to test the algorithms developed in pattern recognition. The unique circular shape of the iris and its time invariance makes it a versatile technique that has an accuracy that can be mathematically proven. Here in this work we propose a new segmentation technique and...
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...
Many computer vision tasks require efficient evaluation of Support Vector Machine (SVM) classifiers on large image databases. Our goal is to efficiently evaluate SVM classifiers on a large number of images. We propose a novel Error Space Encoding (ESE) scheme for SVM evaluation which utilizes large number of classifiers already evaluated on the similar data set. We model this problem as an encoding...
Methods based on Local Binary Patterns have been used successfully in a wide range of texture classification tasks. A restriction shared by all methods based on Local Binary Patterns is the high sensitivity to signal scale. In recent work we presented a general framework for scale-adaptive computation of Local Binary Patterns, improving the accuracy in texture classification scenarios involving varying...
This paper proposes a new system for offline writer identification and writer verification. The proposed method uses GMM supervectors to encode the feature distribution of individual writers. Each supervector originates from an individual GMM which has been adapted from a background model via a maximum-a-posteriori step followed by mixing the new statistics with the background model. We show that...
Breast cancer is considered as the most important cause of death among women. Architectural distortions are very important signs of breast cancer and early detection of them is a rewarding work. In this paper we propose a method to recognize architectural distortion from normal parenchyma. In our proposed method, appropriate features are extracted by the analysis of oriented textures with the application...
Spoofing attacks mainly include printing artifacts, electronic screens and ultra-realistic face masks or models. In this paper, we propose a component-based face coding approach for liveness detection. The proposed method consists of four steps: (1) locating the components of face; (2) coding the low-level features respectively for all the components; (3) deriving the high-level face representation...
Music Information Retrieval (MIR) is a crucial topic in the domain of information retrieval. According to major characteristics of music, Query-by-Humming system retrieves interesting music by finding melody that contains similar or equal melody to the humming query. Basing on the designed fuzzy inference model a novel Query-by-Humming/Singing system is proposed to extract pitch contour information...
This paper presents Locality-constrained Low Rank Coding (LLRC) as a novel approach for image classification. The widely used Sparse representation based algorithms reconstruct a test sample using a sparse linear combination of training samples. But they do not consider the underlying structure of the data in the feature space. On the other hand, Low Rank representation has been recently used for...
One of the major problems in heterogeneous databases integration with different scheme and management system, is the data tautology, which involves an issue of semantics in the content mainly from the names and addresses at the time of normalizing the database, this problem is tackled in this article, by means of a neural network to classify the data. The results show that can be proper classification...
Probabilistic latent semantic analysis is a topic modeling technique to discover the hidden structure in binary and count data. As a mixture model, it performs a probabilistic mixture decomposition on the co-occurrence matrix, which produces two matrices assigned with probabilistic explanations. However, the factorized matrices may be rather smooth, which means we may obtain global feature and topic...
In this paper, we present a novel approach, namely directional multi-mode principal component analysis, which efficiently avoids the small sample size problem and preserves the spatial information embed in among pixels of image, by encoding the input high-dimensional image as a tensor. In the proposed scheme, the mode-k matrix of the image is re-sampled and re-arranged to form a mode-k directional...
Content fingerprinting provides a compact representation of multimedia objects for copy identification. This paper analyzes the impact of the ordinal-ranking based feature encoding on the performance of content fingerprinting. Expressions are derived for the identification performance of a fingerprinting system with and without ordinal ranking. The analysis indicates that when the number of features...
This article proposes a general extension of the Error Correcting Output Codes (ECOC) framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. Validation on UCI database and two real machine vision applications show that the online problem-dependent ECOC proposal provides a feasible and robust way for...
Because of its robustness, user friendliness, low cost and high accuracy, palmprint recognition has been widely studied in the past ten years. Various feature extraction and matching schemes have been proposed, among which the Gabor phase and orientation codes are very effective and efficient for palmprint representation and matching. Although these methods are adopted in the online palmprint recognition...
Nearest neighbor searching (NNS) is a common classification method, but its brute-force (BF) implementation is inefficient for dimensions greater than 10. We present Cellular Class Encoding (CCE), shown to be 1.1-1.7 times faster than BF on real-world, 14-dimensional data sets. Moreover, if applied to bounded sets, CCE is a full-search equivalent to BF. Given a query in an indexed cell of a partitioned...
Developing accurate and robust palmprint verification algorithms is one of the key issues in automatic palmprint recognition systems. Recently, orientation based coding algorithms, such as Competitive Code (CompCode) and Orthogonal Line Ordinal Features (OLOF), have been proposed and have been attracting much research attention. Such algorithms could achieve high accuracy with high feature matching...
Nowadays, security of Web applications faces a threat of script injection attacks. DTP (dynamic taint propagation) and DIFT (dynamic information flow tracking) have been established as powerful techniques to detect script injection attacks. However current DTP/DIFT systems still suffer from tradeoff between false positives and negatives.This paper proposes string-wise information flow tracking, SWIFT...
In this paper, a prediction method of protein contact on the basis of information granules and RBF neural network have been brought forward. This method improved the encoding approach of protein structure data and classifier performance to enhance the predicting accuracy of protein contact. 200 nonhomologous proteins from the PDB database were encoded according to the encoding approach and were taken...
We consider in this paper the problem of large scale natural image classification. As the explosion and popularity of images in the Internet, there are increasing attentions to utilize millions of or even billions of these images for helping image related research. Beyond the opportunities brought by unlimited data, a great challenge is how to design more effective classification methods under these...
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