Semantic Visual Decomposition Modelling for Improving Object Detection in Complex Scene Images

摘要

We propose a systematic method for constructing a compositional model for recognising object instances in images of real life subjects. The model is trained on a set of visual examples of contained in a given image, in order to capture the visual characteristics of the contained objects, and to derive spatial relationships between the internal key sub-components of each object instance. The recognition method focuses on extracting visual similarities at the component level in three feature spaces: histogram of boundary distribution, intensity histogram, and histogram of oriented gradient (HOG). Principle Component Analysis (PCA) is used for the component selection and feature weighting. The proposed recognition method is not only capable of improving the accuracy of popular object detection algorithms, but also offers a systematic way of generating detection models.