2014/11/30 by Neda Pourali
Computer Science · Mathematics · #Advanced Image and Video Retrieval Techniques #Algorithm #Annotation #Artificial intelligence #Automatic image annotation #Computation #Computer science #Curse of dimensionality #Diffusion map #Dimension (graph theory) #Dimensionality reduction #Image (mathematics) #Image Retrieval and Classification Techniques #Image retrieval #Machine learning #Manifold (fluid mechanics) #Mathematics #Nonlinear dimensionality reduction #Pattern recognition (psychology) #Task (project management) #Text and Document Classification Technologies #cs.CV #cs.IR #cs.LG #msc:68T10
paper · pdf · doi:10.5121/ijfcst.2014.4606
11 pages, 8 figures
openalex publication_date 2014/11/30 · arxiv created 2014/12/08 · arxiv updated 2014/12/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.