441 research outputs found
Latent Semantic Learning with Structured Sparse Representation for Human Action Recognition
This paper proposes a novel latent semantic learning method for extracting
high-level features (i.e. latent semantics) from a large vocabulary of abundant
mid-level features (i.e. visual keywords) with structured sparse
representation, which can help to bridge the semantic gap in the challenging
task of human action recognition. To discover the manifold structure of
midlevel features, we develop a spectral embedding approach to latent semantic
learning based on L1-graph, without the need to tune any parameter for graph
construction as a key step of manifold learning. More importantly, we construct
the L1-graph with structured sparse representation, which can be obtained by
structured sparse coding with its structured sparsity ensured by novel L1-norm
hypergraph regularization over mid-level features. In the new embedding space,
we learn latent semantics automatically from abundant mid-level features
through spectral clustering. The learnt latent semantics can be readily used
for human action recognition with SVM by defining a histogram intersection
kernel. Different from the traditional latent semantic analysis based on topic
models, our latent semantic learning method can explore the manifold structure
of mid-level features in both L1-graph construction and spectral embedding,
which results in compact but discriminative high-level features. The
experimental results on the commonly used KTH action dataset and unconstrained
YouTube action dataset show the superior performance of our method.Comment: The short version of this paper appears in ICCV 201
Activity recognition from videos with parallel hypergraph matching on GPUs
In this paper, we propose a method for activity recognition from videos based
on sparse local features and hypergraph matching. We benefit from special
properties of the temporal domain in the data to derive a sequential and fast
graph matching algorithm for GPUs.
Traditionally, graphs and hypergraphs are frequently used to recognize
complex and often non-rigid patterns in computer vision, either through graph
matching or point-set matching with graphs. Most formulations resort to the
minimization of a difficult discrete energy function mixing geometric or
structural terms with data attached terms involving appearance features.
Traditional methods solve this minimization problem approximately, for instance
with spectral techniques.
In this work, instead of solving the problem approximatively, the exact
solution for the optimal assignment is calculated in parallel on GPUs. The
graphical structure is simplified and regularized, which allows to derive an
efficient recursive minimization algorithm. The algorithm distributes
subproblems over the calculation units of a GPU, which solves them in parallel,
allowing the system to run faster than real-time on medium-end GPUs
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