TY - GEN
T1 - Contextual-guided Bag-of-visual-words model for Multi-class object categorization
AU - Mirza-Mohammadi, Mehdi
AU - Escalera, Sergio
AU - Radeva, Petia
PY - 2009
Y1 - 2009
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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70349335789&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-03767-2_91
DO - 10.1007/978-3-642-03767-2_91
M3 - Article in proceeding
AN - SCOPUS:70349335789
SN - 3642037666
SN - 9783642037665
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 748
EP - 756
BT - Computer Analysis of Images and Patterns - 13th International Conference, CAIP 2009, Proceedings
T2 - 13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009
Y2 - 2 September 2009 through 4 September 2009
ER -