Authors:
Abhinaba Roy
1
;
Biplab Banerjee
2
and
Vittorio Murino
1
Affiliations:
1
Istituto Italiano di Tecnologia, Italy
;
2
Indian Institute of Technology, India
Keyword(s):
Action Recognition, Multiple Instance Learning, Dictionary Learning, Support Vector Machines.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Instance-Based Learning
;
Learning of Action Patterns
;
Multi-Instance Learning
;
Pattern Recognition
;
Software Engineering
;
Theory and Methods
Abstract:
In this paper we deal with the problem of action recognition from unconstrained videos under the notion of
multiple instance learning (MIL). The traditional MIL paradigm considers the data items as bags of instances
with the constraint that the positive bags contain some class-specific instances whereas the negative bags
consist of instances only from negative classes. A classifier is then further constructed using the bag level
annotations and a distance metric between the bags. However, such an approach is not robust to outliers and
is time consuming for a moderately large dataset. In contrast, we propose a dictionary learning based strategy
to MIL which first identifies class-specific discriminative codewords, and then projects the bag-level instances
into a probabilistic embedding space with respect to the selected codewords. This essentially generates a fixed-length
vector representation of the bags which is specifically dominated by the properties of the class-specific
instances. We introduce a novel exhaustive search strategy using a support vector machine classifier in order to
highlight the class-specific codewords. The standard multiclass classification pipeline is followed henceforth
in the new embedded feature space for the sake of action recognition. We validate the proposed framework
on the challenging KTH and Weizmann datasets, and the results obtained are promising and comparable to
representative techniques from the literature.
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