779 research outputs found
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
Sparse MDMO: learning a discriminative feature for micro-expression recognition
Micro-expressions are the rapid movements of facial muscles that can be used to reveal concealed emotions. Recognizing them from video clips has a wide range of applications and receives increasing attention recently. Among existing methods, the main directional mean optical-flow (MDMO) feature achieves state-of-the-art performance for recognizing spontaneous micro-expressions. For a video clip, the MDMO feature is computed by averaging a set of atomic features frame-by-frame. Despite its simplicity, the average operation in MDMO can easily lose the underlying manifold structure inherent in the feature space. In this paper we propose a sparse MDMO feature that learns an effective dictionary from a micro-expression video dataset. In particular, a new distance metric is proposed based on the sparsity of sample points in the MDMO feature space, which can efficiently reveal the underlying manifold structure. The proposed sparse MDMO feature is obtained by incorporating this new metric into the classic graph regularized sparse coding (GraphSC) scheme. We evaluate sparse MDMO and four representative features (LBP-TOP, STCLQP, MDMO and FDM) on three spontaneous micro-expression datasets (SMIC, CASME and CASME II). The results show that sparse MDMO outperforms these representative features
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