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The conventional approaches for habitats mapping based on supervised algorithms need a seabed ground truth classes to know the entire seabed types before the training phase. These approaches give satisfying results only when a comprehensive training set is available. If the training set lacks a particular kind of seabed, it will be unknown for the classifier and the classification will be reduced...
Technological advancement had replaced humans with machines in almost every field. Banking automation have reduced human workload by introducing machines. Tedious task like currency handling that require more care are simplified by banking automation. When machines are handling currency they should recognize it. In this paper a method for currency recognition using principal component analysis is...
We propose a new Blind Source Separation technique for whole-brain activity estimation that best profits from FMRI's intrinsic spatial sparsity. The Local Sparse Component Analysis (LSCA) combines wavelet analysis, group-separable regularizers, contiguity-constrained clusterization and principal components analysis (PCA) into a unique spatial sparse representation of FMRI images towards efficient...
This paper proposes to use Genetic algorithm for optimizing the best Eigen vectors to improve the recognition accuracy of Modular image Principal Component Analysis (MIPCA) for face recognition. Modular Image PCA has been proved to be efficient in extracting features for recognizing face invariant to large expression. It is important to note that all the extracted features are not efficient and required...
The use of recommender systems to support users' search and selection of items in an information overloaded environment is widespread. Usually, precision and recall are utilized for evaluating a recommender system. However, an alternative measure should be considered, because a user's satisfaction is the most important factor to be considered when constructing a recommender system. Briefly, there...
It is important for language learners to determine and reflect on their writing errors in order to overcome weaknesses. Each language learner has their own unique writing error characteristics and therefore has different learning needs. In this paper, we analyze the writing errors of foreign language learners on the language learning SNS website Lang-8 to investigate the characteristics of errors...
Based on fitting the Local Binary Patterns (LBP) histogram into the bag-of-words paradigm, we propose an LBP variant termed Principal Local Binary Patterns (PLBP) which are learned in an unsupervised way from the data. The learning problem turns out to be the same as the Principal Component Analysis (PCA) and thus can be solved very efficiently. Unlike the manually specified patterns in LBP which...
This work proposes a low cost alternative for DSP kits for image processing. Here we used friendly arm board with opencv. Friendly arm board is a low cost embedded board with ARM9 as the core where as Opencv is a computer vision simulation library from Intel. In this work the platform for image processing is prepared by porting the opencv library to the ARM9 board. Two algorithms for face recognition...
Glaucoma is the most common cause of blindness and it affects most of the ageing society and this occurs due to pressure increases in the optic nerve which damages the optic nerve. This paper is an attempt to study and analyze the texture features of the Fundus image and its variations when the Fundus image is infected with glaucoma. The texture features extracted are localized around the optic cup...
This paper considers the clustering algorithm based on mixture of probabilistic principle component analyzers in distributed environments. The EM procedure of it is first transformed to a summing variant. Following the classic distributed EM framework for mixture of exponential family distributions and utilizing the summing variant, we propose the distributed EM for mixture of probabilistic principle...
In this paper we introduce a novel method for movement recognition in motion capture data. A movement is regarded as a combination of basic movement patterns, the so-called dynemes. Initially a K-means variant that takes into account the periodic nature of angular data is applied on training data to discover the most discriminative dynemes. Each frame is then assigned to one of these dynemes and a...
Phone Log-Likelihood Ratios (PLLR) have been recently introduced as features for spoken language and speaker recognition systems. This representation has proven to be an effective way of retrieving acoustic-phonotactic information into frame-level vectors, which can be easily plugged into state-of-the-art systems. In a previous work, we began the search of reduced representations of PLLRs, as a mean...
Existing linear projection based hashing methods have witnessed many progresses in finding the approximate nearest neighbor(s) of a given query. They perform well when using a short code. But their code length depends on the original data dimension, thus their performance can not be further improved with higher number of bits for low dimensional data. In addition, in the case of high dimensional data,...
In this paper, we present a simple and efficient way to add supervised information into Fisher vectors, which has become a popular image representation method for image classification and retrieval purposes in recent years. The basic idea of our approach is to improve the Fisher kernel in the training process by adding a discriminative label comparison matrix to it. The resulting new representations,...
This paper addresses the problem of learning semantic compact binary codes for efficient retrieval in large-scale image collections. Our contributions are three-fold. Firstly, we introduce semantic codes, of which each bit corresponds to an attribute that describes a property of an object (e.g. dogs have furry). Secondly, we propose to use matrix factorization (MF) to learn the semantic codes by encoding...
In this paper we describe a Fisher vector encoding of images over Random Density Forests. Random Density Forests (RDFs) are an unsupervised variation of Random Decision Forests for density estimation. In this work we train RDFs by splitting at each node in order to minimize the Gaussian differential entropy of each split. We use this as generative model of image patch features and derive the Fisher...
The advent of near infrared imagery and it's applications in face recognition has instigated research in cross spectral (visible to near infrared) matching. Existing research has focused on extracting textural features including variants of histogram of oriented gradients. This paper focuses on studying the effectiveness of these features for cross spectral face recognition. On NIR-VIS-2.0 cross spectral...
We show that dually supervised manifold embedding can improve the performance of machine learning based person-independent and thus calibration-free gaze estimation. For this purpose, we perform a manifold embedding for each person in the training dataset and then learn a linear transformation that aligns the individual, person-dependent manifolds. We evaluate the effect of manifold alignment on the...
In this paper we propose a novel dimensionality reduction method that is based on successive Laplacian SVM projections in orthogonal deflated subspaces. The proposed method, called Laplacian Support Vector Analysis, produces projection vectors, which capture the discriminant information that lies in the subspace orthogonal to the standard Laplacian SVMs. We show that the optimal vectors on these deflated...
Principal component analysis (PCA) is used in diverse settings for dimensionality reduction. If data elements are all the same size, there are many approaches to estimating the PCA decomposition of the dataset. However, many datasets contain elements of different sizes that must be coerced into a fixed size before analysis. Such approaches introduce errors into the resulting PCA decomposition. We...
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