TY - GEN
T1 - Classification with global, local and shared features
AU - Bilen, Hakan
AU - Namboodiri, Vinay P.
AU - Van Gool, Luc J.
PY - 2012/9/11
Y1 - 2012/9/11
N2 - We present a framework that jointly learns and then uses multiple image windows for improved classification. Apart from using the entire image content as context, class-specific windows are added, as well as windows that target class pairs. The location and extent of the windows are set automatically by handling the window parameters as latent variables. This framework makes the following contributions: a) the addition of localized information through the class-specific windows improves classification, b) windows introduced for the classification of class pairs further improve the results, c) the windows and classification parameters can be effectively learnt using a discriminative max-margin approach with latent variables, and d) the same framework is suited for multiple visual tasks such as classifying objects, scenes and actions. Experiments demonstrate the aforementioned claims.
AB - We present a framework that jointly learns and then uses multiple image windows for improved classification. Apart from using the entire image content as context, class-specific windows are added, as well as windows that target class pairs. The location and extent of the windows are set automatically by handling the window parameters as latent variables. This framework makes the following contributions: a) the addition of localized information through the class-specific windows improves classification, b) windows introduced for the classification of class pairs further improve the results, c) the windows and classification parameters can be effectively learnt using a discriminative max-margin approach with latent variables, and d) the same framework is suited for multiple visual tasks such as classifying objects, scenes and actions. Experiments demonstrate the aforementioned claims.
UR - http://www.scopus.com/inward/record.url?scp=84865824404&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-32717-9_14
DO - 10.1007/978-3-642-32717-9_14
M3 - Chapter in a published conference proceeding
AN - SCOPUS:84865824404
SN - 9783642327162
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 134
EP - 143
BT - Pattern Recognition - Joint 34th DAGM and 36th OAGM Symposium, Proceedings
T2 - Joint 34th Symposium of the German Association for Pattern Recognition, DAGM 2012 and 36th Symposium of the Austrian Association for Pattern Recognition, OAGM 2012
Y2 - 28 August 2012 through 31 August 2012
ER -