Indoor image representation by high-level semantic features

Indoor image features extraction is a fundamental problem in multiple fields such as image processing, pattern recognition, robotics, and so on. Nevertheless, most of the existing feature extraction methods, which extract features based on pixels, color, shape/object parts or objects on images, suffer from limited capabilities in describing semantic information (e.g., object association). These techniques, therefore, involve undesired classification performance. To tackle this issue, we propose the notion of high-level semantic features and design four steps to extract them. Specifically, we first construct the objects pattern dictionary through extracting raw objects in the images, and then retrieve and extract semantic objects from the objects pattern dictionary. We finally extract our high-level semantic features based on the calculated probability and delta parameter. The experiments on three publicly available datasets (MIT-67, Scene15, and NYU V1) show that our feature extraction approach outperforms the state-of-the-art feature extraction methods for indoor image classification, given a lower dimension of our features than those methods.