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Covariance matrix has recently received increasing attention in computer vision by leveraging Riemannian geometry of symmetric positive-definite (SPD) matrices. Originally proposed as a region descriptor, it has now been used as a generic representation in various recognition tasks. However, covariance matrix has shortcomings such as being prone to be(More)
Efficient Human Epithelial-2 cell image classification can facilitate the diagnosis of many autoimmune diseases. This paper proposes an automatic framework for this classification task, by utilizing the deep convolutional neural networks (CNNs) which have recently attracted intensive attention in visual recognition. In addition to describing the proposed(More)
Stein kernel (SK) has recently shown promising performance on classifying images represented by symmetric positive definite (SPD) matrices. It evaluates the similarity between two SPD matrices through their eigenvalues. In this paper, we argue that directly using the original eigenvalues may be problematic because: 1) eigenvalue estimation becomes biased(More)
Bag-of-words model (BoW), inspired by the problem of text representation and classification, has attracted intensive attention in object and scene categorization for its flexibility and good performance. In BOW model, a visual vocabulary is obtained by clustering local patches detected from training image set, and then an image can be represented by the(More)
Recently, a sparse inverse covariance estimation (SICE) technique has been employed to model functional brain connectivity. The inverse covariance matrix (SICE matrix in short) estimated for each subject is used as a representation of brain connectivity to discriminate Alzheimers disease from normal controls. However, we observed that direct use of the SICE(More)
Purpose – To introduce chromatic methods and their application to monitoring complex systems. Design/methodology/approach – Complexity is introduced and the need for holistic monitoring is suggested. The chromatic methodologies are introduced and their broad-ranging applicability is illustrated with reference to a number of diverse applications. Findings –(More)
Distributional word clustering merges the words having similar probability distributions to attain reliable parameter estimation, compact classification models and even better classification performance. Agglomerative Information Bottleneck (AIB) is one of the typical word clustering algorithms and has been applied to both traditional text classification(More)
Word clustering is an effective approach in the bag- of-words model to reducing the dimensionality of high-dimensional features. In recent years, the bag- of-words model has been successfully introduced into visual recognition and significantly developed. Often, in order to adequately model the complex and diversified visual patterns, a large number of(More)