Semi-automatic image annotation

Abstract

High quality ground truth data is essential for the development of image recognition systems. General purpose datasets are widely used in research, but they are not suitable as training sets for specialized real-world recognition tasks. The manual annotation of custom ground truth data sets is expensive, but machine learning techniques can be applied to preprocess image data and facilitate annotation. We propose a semi-automatic image annotation process, which clusters images according to similarity in a bag-of-features (BoF) approach. Clusters of images can be efficiently annotated in one go. The system recalculates the clustering continuously, based on partial annotations provided during annotation, by weighting BoF vector elements to increase intra-cluster similarity. Visualization of top-weighted codebook elements allows users to estimate the quality of annotations and of the recalculated clustering. © 2013 Springer-Verlag.