Structured Kernel Dictionary Learning With Correlation Constraint for Object Recognition

doi:10.1109/TIP.2017.2718187

Return

Structured Kernel Dictionary Learning With Correlation Constraint for Object Recognition

2017-09-01

PRE

DOI：10.1109/TIP.2017.2718187

Original

Original request for help

Save

Abstract

En 中文

In this paper, we propose a new discriminative non-linear dictionary learning approach, called correlation constrained structured kernel KSVD, for object recognition. The objective function for dictionary learning contains a reconstructive term and a discriminative term. In the reconstructive term, signals are implicitly non-linearly mapped into a space, where a structured kernel dictionary, each sub-dictionary of which lies in the span of the mapped signals from the corresponding class, is established. In the discriminative term, by analyzing the classification mechanism, the correlation constraint is proposed in kernel form, constraining the correlations between different discriminative codes, and restricting the coefficient vectors to be transformed into a feature space, where the features are highly correlated inner-class and nearly independent between-classes. The objective function is optimized by the proposed structured kernel KSVD. During the classification stage, the specific form of the discriminative feature is needless to be known, while the inner product of the discriminative feature with kernel matrix embedded is available, and is suitable for a linear SVM classifier. Experimental results demonstrate that the proposed approach outperforms many state-of-the-art dictionary learning approaches for face, scene, and synthetic aperture radar vehicle target recognition.

Keywords:

Correlation constraint

discriminative code

kernel method

KSVD

non-linear dictionary learning

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.