Multi-layer Graph Constraint Dictionary Pair Learning for Image Classification

Multi-layer dictionary learning (MDL) has demonstrated significantly improved performance for image classification. However, most of the existing MDL methods just overall shared dictionary learning architecture, which weakens the discrimination ability of the dictionaries. For this, we proposed a powerful framework called the Multi-layer Graph Constraint Dictionary Pair Learning (MGDPL). Our MGDPL integrates multi-layer dictionary pair learning, structure graph constraint, and discrimination sparse representations into a unified framework. First, the multi-layer structured dictionary learning mechanism is applied to dictionary pairs to enhance the discrimination performance by rebuilding the reconstruction error of the previous layer via the latter layer. Second, it subjects the structure graph constraint on the sub-sparse representations to ensure the discrimination capability of the near neighbor graph. Third, the multi-layer discriminant graph regularized constraint term can ensure high intra-class tightness and inter-class dispersion of dictionary atoms in reconstruction space. Extensive experiments show that MGDPL can achieve excellent performance over other state-of-the-arts.