UNSUPERVISED FEATURE LEARNING FOR MID-LEVEL DATA REPRESENTATION

Year 2016, Volume: 12 Issue: 1, 51 - 79, 24.04.2016

Emrah Ergül Mehmet Karayel Oğuzhan Timuş Erkan Kıyak Turkish Naval Academy

Abstract

Attribute based approaches are commonly used in recent
years instead of  low level features for
image classification which is one of the most important problems in the field
of computer vision. The most important advantage of attribute based approach is
that learning can be performed similar to human by using attributes which makes
sense for people. In this study, unsupervised attributes are developed in order
to avoid human related problems in supervised attribute learning. In our
proposed work, the attributes are generated as random binary and relative
definitions. The process of random attribute generation simplifies the data
modeling when compared to other work in the literature. In addition, a major
problem which is the increasing the numbers of attributes in attribute based
approaches is eliminated owing to the increasing the numbers of attributes
easily. Furthermore, attributes are selected more wisely using simple
applicable algorithm to improve the discriminative capacity of randomly
generated attribute set for image classification. The proposed approaches are
evaluated with the other similar attribute based studies comparatively in the
literature based on the same data set (OSR-Open Scene Recognition). Experiments
show that noteworthy performance increase is achieved.

Keywords

Relative Attributes, Unsupervised Feature Extraction, Attribute Selection, Visual Recognition

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