Supervised dictionary learning for action recognition and localization
Publisher
Metadata
Show full item recordAbstract
Image sequences with humans and human activities are everywhere.
With the amount of produced and distributed data increasing at an
unprecedented rate, there has been a lot of interest in building systems
that can understand and interpret the visual data, and in particular detect
and recognise human actions. Dictionary based approaches learn a
dictionary from descriptors extracted from the videos in the first stage
and a classifier or a detector in the second stage. The major drawback
of such an approach is that the dictionary is learned in an unsupervised
manner without considering the task (classification or detection) that
follows it. In this work we develop task dependent(supervised) dictionaries
for action recognition and localization, i.e., dictionaries that are
best suited for the subsequent task. In the first part of the work, we
propose a supervised max-margin framework for linear and non-linear
Non-Negative Matrix Factorization (NMF). To achieve this, we impose
max-margin constraints within the formulation of NMF and simultaneously
solve for the classifier and the dictionary. The dictionary (basis
matrix) thus obtained maximizes the margin of the classifier in the low
dimensional space (in the linear case) or in the high dimensional feature
space (in the non-linear case). In the second part the work, we
develop methodologies for action localization. We first propose a dictionary
weighting approach where we learn local and global weights for
the dictionary by considering the localization information of the training
sequences. We next extend this approach to learn a task-dependent
dictionary for action localization that incorporates the localization information
of the training sequences into dictionary learning. The results
on publicly available datasets show that the performance of the system
is improved by using the supervised information while learning dictionary.
Authors
Kumar, B. G. VijayCollections
- Theses [3834]