Human Detection in Video Using Poselet Combine with Gradient Local Auto Correlation Classifier

Tran, Thuong-Khanh; Bui, Ngoc-Nam; Kim, Jin‐Young

doi:10.1109/icitcs.2014.7021775

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2015

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“…The GLAC descriptor, which is based on the second order of statistics of gradients (spatial and orientational auto-correlations of local image gradients), can effectively capture rich information from images and has been successfully used in motion recognition [22] and human detection [15,23]. To our best knowledge, this is the first time, image gradient based features have been used for hyperspectral image classification.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral image classification using Gradient Local Auto-Correlations

Chen

Jiang

Zhang

et al. 2015

2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)

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Spatial information has been verified to be helpful in hyperspectral image classification. In this paper, a spatial feature extraction method utilizing spatial and orientational auto-correlations of image local gradients is presented for hyperspectral imagery (HSI) classification. The Gradient Local Auto-Correlations (GLAC) method employs second order statistics (i.e., auto-correlations) to capture richer information from images than the histogram-based methods (e.g., Histogram of Oriented Gradients) which use first order statistics (i.e., histograms). The experiments carried out on two hyperspectral images proved the effectiveness of the proposed method compared to the state-of-the-art spatial feature extraction methods for HSI classification.

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Section: Introductionmentioning

confidence: 99%