Authors:
Przemysław Górecki
;
Piotr Artiemjew
;
Paweł Drozda
and
Krzysztof Sopyła
Affiliation:
University of Warmia and Mazury, Poland
Keyword(s):
Visual dictionaries, Classification, Bag of words, SVM, SIFT.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Hybrid Intelligent Systems
;
Knowledge Representation and Reasoning
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
;
Vision and Perception
Abstract:
This paper studies the problem of visual subcategorization of objects within a larger category. Such categorization seems more challenging than categorization of objects from visually distinctive categories, previously presented in the literature. The proposed methodology is based on ”Bag of Visual Words” using Scale-Invariant Feature Transform (SIFT) descriptors and Support Vector Machines (SVM). We present the results of the experimental session, both for categorization of visually similar and visually distinctive objects. In addition, we attempt to empirically identify the most effective visual dictionary size and the feature vector normalization scheme.