Part detection is an important aspect of object recognition. Most approaches apply object proposals to generate hundreds of possible part bounding box candidates which are then evaluated by part classifiers. Recently several methods have investigated directly regressing to a limited set of bounding boxes from deep neural network representation. However, for object parts such methods may be unfeasible due to their relatively small size with respect to the image. We propose a hierarchical method for object and part detection.
Hierarchical part detection with deep neural networks / Cervantes, Esteve; Yu, Long Long; Bagdanov, Andrew D.; Masana, Marc; Van De Weijer, Joost. - ELETTRONICO. - 2016-:(2016), pp. 1933-1937. (Intervento presentato al convegno 23rd IEEE International Conference on Image Processing, ICIP 2016 tenutosi a Phoenix Convention Center, usa nel 2016) [10.1109/ICIP.2016.7532695].
Hierarchical part detection with deep neural networks
BAGDANOV, ANDREW DAVID;
2016
Abstract
Part detection is an important aspect of object recognition. Most approaches apply object proposals to generate hundreds of possible part bounding box candidates which are then evaluated by part classifiers. Recently several methods have investigated directly regressing to a limited set of bounding boxes from deep neural network representation. However, for object parts such methods may be unfeasible due to their relatively small size with respect to the image. We propose a hierarchical method for object and part detection.
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