724 research outputs found
Action Classification with Locality-constrained Linear Coding
We propose an action classification algorithm which uses Locality-constrained
Linear Coding (LLC) to capture discriminative information of human body
variations in each spatiotemporal subsequence of a video sequence. Our proposed
method divides the input video into equally spaced overlapping spatiotemporal
subsequences, each of which is decomposed into blocks and then cells. We use
the Histogram of Oriented Gradient (HOG3D) feature to encode the information in
each cell. We justify the use of LLC for encoding the block descriptor by
demonstrating its superiority over Sparse Coding (SC). Our sequence descriptor
is obtained via a logistic regression classifier with L2 regularization. We
evaluate and compare our algorithm with ten state-of-the-art algorithms on five
benchmark datasets. Experimental results show that, on average, our algorithm
gives better accuracy than these ten algorithms.Comment: ICPR 201
Multiple instance learning for sequence data with across bag dependencies
In Multiple Instance Learning (MIL) problem for sequence data, the instances
inside the bags are sequences. In some real world applications such as
bioinformatics, comparing a random couple of sequences makes no sense. In fact,
each instance may have structural and/or functional relations with instances of
other bags. Thus, the classification task should take into account this across
bag relation. In this work, we present two novel MIL approaches for sequence
data classification named ABClass and ABSim. ABClass extracts motifs from
related instances and use them to encode sequences. A discriminative classifier
is then applied to compute a partial classification result for each set of
related sequences. ABSim uses a similarity measure to discriminate the related
instances and to compute a scores matrix. For both approaches, an aggregation
method is applied in order to generate the final classification result. We
applied both approaches to solve the problem of bacterial Ionizing Radiation
Resistance prediction. The experimental results of the presented approaches are
satisfactory
Analysing the behaviour of robot teams through relational sequential pattern mining
This report outlines the use of a relational representation in a Multi-Agent
domain to model the behaviour of the whole system. A desired property in this
systems is the ability of the team members to work together to achieve a common
goal in a cooperative manner. The aim is to define a systematic method to
verify the effective collaboration among the members of a team and comparing
the different multi-agent behaviours. Using external observations of a
Multi-Agent System to analyse, model, recognize agent behaviour could be very
useful to direct team actions. In particular, this report focuses on the
challenge of autonomous unsupervised sequential learning of the team's
behaviour from observations. Our approach allows to learn a symbolic sequence
(a relational representation) to translate raw multi-agent, multi-variate
observations of a dynamic, complex environment, into a set of sequential
behaviours that are characteristic of the team in question, represented by a
set of sequences expressed in first-order logic atoms. We propose to use a
relational learning algorithm to mine meaningful frequent patterns among the
relational sequences to characterise team behaviours. We compared the
performance of two teams in the RoboCup four-legged league environment, that
have a very different approach to the game. One uses a Case Based Reasoning
approach, the other uses a pure reactive behaviour.Comment: 25 page
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