Multi-class Binary Object Categorization Using Blurred Shape Models

The main difficulty in the binary object classification field lays in dealing with a high variability of symbol appearance. Rotation, partial occlusions, elastic deformations, or intra-class and inter-class variabilities are just a few problems. In this paper, we introduce a novel object description for this type of symbols. The shape of the object is aligned based on principal components to make the recognition invariant to rotation and reflection. We propose the Blurred Shape Model (BSM) to describe the binary objects. This descriptor encodes the probability of appearance of the pixels that outline the object’s shape. Besides, we present the use of this descriptor in a system to improve the BSM performance and deal with binary objects multi-classification problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split object classes. Then, the different binary problems learned by the Adaboost are embedded in the Error Correcting Output ...

• Type: Conference
• Year: 2007
• Where: CIARP
• Authors: Sergio Escalera, Alicia Fornés, Oriol Pujol, Josep Lladós, Petia Radeva
• Keywords: Binary Object Classification, Binary Objects, CIARP 2007, Object Classes