Web image annotation by diffusion maps manifold learning algorithm

TL;DR

This paper explores using diffusion maps manifold learning to reduce feature dimensions, improving web image annotation efficiency and effectiveness on large datasets.

Contribution

It introduces the application of diffusion maps for dimensionality reduction in web image annotation, demonstrating its effectiveness through extensive experiments.

Findings

Diffusion maps effectively reduce feature dimensions in image data.

The method improves annotation accuracy and computational efficiency.

Experiments on NUS-WIDE-LITE dataset validate the approach.

Abstract

Automatic image annotation is one of the most challenging problems in machine vision areas. The goal of this task is to predict number of keywords automatically for images captured in real data. Many methods are based on visual features in order to calculate similarities between image samples. But the computation cost of these approaches is very high. These methods require many training samples to be stored in memory. To lessen this burden, a number of techniques have been developed to reduce the number of features in a dataset. Manifold learning is a popular approach to nonlinear dimensionality reduction. In this paper, we investigate Diffusion maps manifold learning method for web image auto-annotation task. Diffusion maps manifold learning method is used to reduce the dimension of some visual features. Extensive experiments and analysis on NUS-WIDE-LITE web image dataset with different visual features show how this manifold learning dimensionality reduction method can be applied effectively to image annotation.