A New Biologically Inspired Feature for Scene Image Classification

Scene classification is a hot topic in pattern recognition and computer vision area. In this paper, based on the past research on vision neuroscience, we proposed a new biologically inspired feature method for scene image classification. The new feature accounts for the visual processing from simple cell to complex cell in V1 area, and also the spatial layout for scene gist signature. It provides a different line and model revision to consider some nonlinearities in V1 area. We compare it with traditional HMAX model and recently proposed ScSPM model, and experiment on a popular 15 scenes dataset. We show that our proposed method has many important differences and merits. The experiment results also show that our method outperforms the state-of-the-art like ScSPM and KSPM model.