Web image annotation by diffusion maps manifold learning algorithm
This is an incremental improvement for web image annotation systems, addressing storage and computation bottlenecks.
The paper tackles the high computational cost of automatic web image annotation by applying diffusion maps manifold learning to reduce the dimensionality of visual features, showing effectiveness on the NUS-WIDE-LITE dataset.
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.