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This paper presents a feature extraction method for three-way data: the cubic higher-order local auto-correlation (CHLAC) method. This method is particularly suitable for analysis of motion-image sequences. Motion-image sequences can be regarded as three-way data consisting of x-, y-and taxes. The CHLAC method is based on three-way auto-correlations of(More)
In this paper, we propose a method to extract features from three-dimensional acceleration signals. The proposed method is based on the (auto-)correlation matrix of Fourier transform features, naturally containing the correlations between the frequencies as well as the ordinary power spectrum for each frequency. The proposed features are inherently(More)
We propose a new method – Cubic Higher-order Local Auto-Correlation (CHLAC) – to address three-way data analysis. This method is a natural extension of Higher-order Local Auto-Correlation (HLAC) [6], which deals only with two-way data. Both methods use " correlation " to summarize relative positions or motions within a local data region , and these can be(More)
In contrast to category-level or cluster-level classifiers, exemplar SVM [17] is successfully applied to classifying (or detecting) a target object as well as transferring instance-level annotations. The method, however, is formulated in a highly biased classification problem where only one positive sample is contrasted with a substantial number of negative(More)
Semantic labelling of acoustic scenes has recently emerged as active topic covering a wide range of applications, e.g. surveillance and audio-based information retrieval. In this paper, we present an effective approach for acoustic scene classification through characterizing both background sound textures and acoustic events. The work takes inspiration from(More)
Image classification methods have been significantly developed in the last decade. Most methods stem from bag-of-features (BoF) approach and it is recently extended to a vector aggregation model, such as using Fisher kernels. In this paper, we propose a novel feature extraction method for image classification. Following the BoF approach, a plenty of local(More)
Histogram-based features have significantly contributed to recent development of image classifications, such as by SIFT local descriptors. In this paper, we propose a method to efficiently transform those histogram features for improving the classification performance. The (L1-normalized) histogram feature is regarded as a probability mass function, which(More)
In this paper, we propose a motion recognition scheme based on a novel method of motion feature extraction. The feature extraction method utilizes auto-correlations of space–time gradients of three-dimensional motion shape in a video sequence. The method effectively exploits the local relationships of the gradients corresponding to the space–time geometric(More)
—We propose a method of clustering sample vectors on a hypersphere. Sample vectors are normalized in many cases, especially when applying kernel functions, and thus lie on a (unit) hypersphere. Considering the constraint of the hypersphere, the proposed method utilizes the von Mises-Fisher distribution in the framework of mean shift. It is also extended to(More)