A Novel Image Annotation Method based on Kernel Methods for Structured Prediction

Abstract

With the development of image processing and storage technology, rapid classification and annotation of huge volumes of digital images have been attracted much attention. However, the complex and ambiguous relationship between images and concept classes poses significant challenges on building effective annotation models. Structured machine learning methods have been studied to tackle the problem of complex relationship between concept classes for prediction, which have been proved effective for image understanding tasks. We proposed a novel image annotation model based on structured machine learning, by introducing a learned kernel function in the sample space, aiming at capturing the underlying distribution of concept classes of the training data set. The model is evaluated on two benchmark data sets and the results show that the model is promising compared to current state-of-the-art methods.