Generalized Adaptive Dictionary Learning via Domain Shift Minimization

TL;DR

This paper introduces a novel method for learning adaptive dictionaries across multiple domains by projecting data into a low-dimensional space, minimizing domain shift, and preserving data structure, improving image classification performance.

Contribution

It proposes a generalized approach for domain-adaptive dictionary learning that incorporates domain shift minimization and structure preservation, with a variant for class-specific dictionaries.

Findings

Outperforms state-of-the-art methods in image classification.

Effectively reduces domain shift in multi-domain data.

Enhances classification accuracy with adaptive, structure-preserving dictionaries.

Abstract

Visual data driven dictionaries have been successfully employed for various object recognition and classification tasks. However, the task becomes more challenging if the training and test data are from contrasting domains. In this paper, we propose a novel and generalized approach towards learning an adaptive and common dictionary for multiple domains. Precisely, we project the data from different domains onto a low dimensional space while preserving the intrinsic structure of data from each domain. We also minimize the domain-shift among the data from each pair of domains. Simultaneously, we learn a common adaptive dictionary. Our algorithm can also be modified to learn class-specific dictionaries which can be used for classification. We additionally propose a discriminative manifold regularization which imposes the intrinsic structure of class specific features onto the sparse coefficients. Experiments on image classification show that our approach fares better compared to the existing state-of-the-art methods.