Authors:
Christina Chrysouli
;
Vasileios Gavriilidis
and
Anastasios Tefas
Affiliation:
Aristotle University of Thessaloniki, Greece
Keyword(s):
Activity Learning, Random Walk Kernel, Support Vector Machines, Feature Extraction.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Support Vector Machines and Applications
;
Theory and Methods
Abstract:
In this paper, we propose a novel graph-based kernel method in order to construct histograms for a bag of words approach, by using similarity measures, applied in activity recognition problems. Bag of words is the most popular framework for performing classification on video data. This framework, however, is an orderless collection of features. We propose a better way to encode action in videos, via altering the histograms. The creation of such histograms is performed based on kernel methods, inspired from graph theory, computed with no great additional computational cost. Moreover, when using the proposed algorithm to construct the histograms, a richer representation of videos is attained. Experiments on folk dances recognition were conducted based on our proposed method, by comparing histograms extracted from a typical bag-of-words framework against histograms of the proposed method, which provided promising results on this challenging task.