Multiview Hessian Discriminative Sparse Coding for Image Annotation

TL;DR

This paper introduces multiview Hessian discriminative sparse coding (mHDSC), a novel method combining Hessian regularization with discriminative sparse coding to improve image annotation by leveraging multiview features and manifold geometry.

Contribution

It proposes a new multiview sparse coding algorithm that integrates Hessian regularization and label information for enhanced image annotation performance.

Findings

mHDSC outperforms existing methods on PASCAL VOC'07 dataset.

Hessian regularization improves the model's ability to capture manifold structure.

Incorporating label information as a view enhances discriminative power.

Abstract

Sparse coding represents a signal sparsely by using an overcomplete dictionary, and obtains promising performance in practical computer vision applications, especially for signal restoration tasks such as image denoising and image inpainting. In recent years, many discriminative sparse coding algorithms have been developed for classification problems, but they cannot naturally handle visual data represented by multiview features. In addition, existing sparse coding algorithms use graph Laplacian to model the local geometry of the data distribution. It has been identified that Laplacian regularization biases the solution towards a constant function which possibly leads to poor extrapolating power. In this paper, we present multiview Hessian discriminative sparse coding (mHDSC) which seamlessly integrates Hessian regularization with discriminative sparse coding for multiview learning problems. In particular, mHDSC exploits Hessian regularization to steer the solution which varies smoothly along geodesics in the manifold, and treats the label information as an additional view of feature for incorporating the discriminative power for image annotation. We conduct extensive experiments on PASCAL VOC'07 dataset and demonstrate the effectiveness of mHDSC for image annotation.