Efficient Local Feature Encoding for Human Action Recognition with Approximate Sparse Coding
نویسندگان
چکیده
منابع مشابه
Efficient Local Feature Encoding for Human Action Recognition with Approximate Sparse Coding
Local spatio-temporal features are popular in the human action recognition task. In practice, they are usually coupled with a feature encoding approach, which helps to obtain the video-level vector representations that can be used in learning and recognition. In this paper, we present an efficient local feature encoding approach, which is called Approximate Sparse Coding (ASC). ASC computes the...
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By extracting local spatial-temporal features from videos, many recently proposed approaches for action recognition achieve promising performance. The Bag-of-Words (BoW) model is commonly used in the approaches to obtain the video level representations. However, BoW model roughly assigns each feature vector to its closest visual word, therefore inevitably causing nontrivial quantization errors ...
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ژورنال
عنوان ژورنال: IEICE Transactions on Information and Systems
سال: 2016
ISSN: 0916-8532,1745-1361
DOI: 10.1587/transinf.2015edp7333