TY  - CONF
AU  - Mehdi Mirza-Mohammadi
AU  - Sergio Escalera
AU  - Petia Radeva
A2  - CAIP
PY  - 2009//
TI  - Contextual-Guided Bag-of-Visual-Words Model for Multi-class Object Categorization
T2  - LNCS
BT  - 13th International Conference on Computer Analysis of Images and Patterns
SP  - 748–756
VL  - 5702
PB  - Springer Berlin Heidelberg
N2  - Bag-of-words model (BOW) is inspired by the text classification problem, where a document is represented by an unsorted set of contained words. Analogously, in the object categorization problem, an image is represented by an unsorted set of discrete visual words (BOVW). In these models, relations among visual words are performed after dictionary construction. However, close object regions can have far descriptions in the feature space, being grouped as different visual words. In this paper, we present a method for considering geometrical information of visual words in the dictionary construction step. Object interest regions are obtained by means of the Harris-Affine detector and then described using the SIFT descriptor. Afterward, a contextual-space and a feature-space are defined, and a merging process is used to fuse feature words based on their proximity in the contextual-space. Moreover, we use the Error Correcting Output Codes framework to learn the new dictionary in order to perform multi-class classification. Results show significant classification improvements when spatial information is taken into account in the dictionary construction step.
SN  - 0302-9743
SN  - 978-3-642-03766-5
UR  - http://dx.doi.org/10.1007/978-3-642-03767-2_91
N1  - HuPBA; MILAB
ID  - Mehdi Mirza-Mohammadi2009
ER  -