Linear discrimination dictionary learning for shape descriptors

The complexity and variation of 3D models have posed a lot of challenges in 3D shape retrieval area, for example, the invariant representation and retrieval of nonrigid and noisy 3D shapes. This paper proposed a supervised dictionary learning scheme called Linear Discrimination Dictionary Learning (LDDL) which can learn shape representations that are insensitive to 3D shape deformations in the same category and different for shapes from different categories in the meantime. Besides, it can extract the subtle differences between 3D shapes for fine-grained shapes. To be specific, in this paper, category-specific dictionaries are learnt to encode subtle visual differences of shapes among different categories, a shared dictionary is learnt to encode common patterns of shapes among all the categories; with the Linear Discriminant Analysis (LDA) constraint on the learnt descriptors, the new descriptors can have small within-class scatter and big between-class scatter. Our method is efficient in training and can obtain promising shape retrieval performance on representative shape benchmark datasets.