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Dictionary based pooling for object categorization
Year: 2014 Keywords: Dictionary Based Image Pooling, Sparse Representation, Object Categorization, iCub, iCubWorld Data-Set
Authors: Sean Ryan Fanello, Nicoletta Noceti, Giorgio Metta, Francesca Odone  
Book title: VISAPP: : International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications.
   
Abstract:
t is well known that image representations learned through ad-hoc dictionaries improve the overall results in object categorization problems. Following the widely accepted coding-pooling visual recognition pipeline, these representations are often tightly coupled with a coding stage. In this paper we show how to exploit ad- hoc representations both within the coding and the pooling phases. We learn a dictionary for each object class and then use local descriptors encoded with the learned atoms to guide the pooling operator. We exhaustively evaluate the proposed approach in both single instance object recognition and object categorization problems. From the applications standpoint we consider a classical image retrieval scenario with the Caltech 101, as well as a typical robot vision task with data acquired by the iCub humanoid robot.
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