Exploring feature dimensionality reduction methods for enhancing automatic sport image annotation

Abstract

Nowadays, multimedia information requires the demand to investigate and apply efficient techniques for better annotation and retrieval purposes. In the content-based indexing, low-level features are generally extracted from images to serve as image descriptors. Other than the descriptor poses a computational overhead, the learning model may also tend to overfit, resulting in performance degeneration. This work solves such problems in the sport image domain by proposing feature dimensionality reduction techniques for the retrieval and annotation of image datasets. Different techniques are investigated, such as Information Gain, Gain Ratio, Chi-Square, and Latent Semantic Analysis (LSA), and applied for sport images classification using Support Vector Machine (SVM) classifier. A comparison between the performances of applying SVM alone and when incorporating the different reduction methods is presented. Experimental results show that the SVM classification accuracy is 76.4%; while integrating LSA technique manages to raise the accuracy to 96%, with the other techniques recording 74% accuracy at 50% feature space reduction.