# Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification

## TL;DR

This paper introduces a novel multi-view vector-valued manifold regularization method that effectively integrates multiple features and label relationships for improved multi-label image classification.

## Contribution

It proposes MV³MR, a new framework that leverages matrix-valued kernels to exploit feature complementarity and label structure in multi-label image classification.

## Key findings

- MV³MR outperforms existing methods on PASCAL VOC'07 and MIR Flickr datasets.
- The method effectively captures the intrinsic local geometry of multi-view data.
- Experimental results demonstrate significant accuracy improvements.

## Abstract

In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e.g. pedestrian, bicycle and tree) and is properly characterized by multiple visual features (e.g. color, texture and shape). Currently available tools ignore either the label relationship or the view complementary. Motivated by the success of the vector-valued function that constructs matrix-valued kernels to explore the multi-label structure in the output space, we introduce multi-view vector-valued manifold regularization (MV$\mathbf{^3}$MR) to integrate multiple features. MV$\mathbf{^3}$MR exploits the complementary property of different features and discovers the intrinsic local geometry of the compact support shared by different features under the theme of manifold regularization. We conducted extensive experiments on two challenging, but popular datasets, PASCAL VOC' 07 (VOC) and MIR Flickr (MIR), and validated the effectiveness of the proposed MV$\mathbf{^3}$MR for image classification.