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In this paper, we introduce a novel local feature-based hierarchical framework to produce invariant sparse codes for object recognition. In order to enforce the invariant property for each sample patch (local feature descriptor) in the image, its sparse code is recovered with a dedicated dictionary whose atoms are adaptively chosen from several bags of candidate atoms. The single-layer invariant sparse...
Low-level feature encoding combined with Spatial Pyramid Matching (SPM) is widely adopted in the image classification system nowadays to extract features, which are usually high-dimensional. This not only makes the classification problem computationally prohibitive, but also raises other issues, such as the “curse of dimensionality”. In this paper we present supervised dimensionality reduction (DR)...
We investigate the problem of human action recognition by studying the effects of fusing feature streams retrieved from color and depth sequences. Our main contribution is two-fold: First, we present the so-called 3DS-HONV descriptor which is a spatio-temporal extension of Histogram of Oriented Normal vector (HONV), specifically designed for capturing the joint shape-motion vision cues from depth...
This paper proposes a novel feature extraction technique for speech recognition based on the principles of sparse coding. The idea is to express a spectro-temporal pattern of speech as a linear combination of an overcomplete set of basis functions such that the weights of the linear combination are sparse. These weights (features) are subsequently used for acoustic modeling. We learn a set of overcomplete...
This paper proposes a stochastic framework for detecting anomalies or gathering interesting events in a noisy environment using a sensor network consisting of binary sensors. A binary sensor is an extremely coarse sensor, capable of measuring data to only 1-bit accuracy. Our proposed stochastic framework employs a large number of cheap binary sensors operating in a noisy environment, yet collaboratively...
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