Hierarchical classifier with multiple feature weighted fusion for scene recognition

Recently, scene recognition is becoming an additional function in digital camera. Automatic scene understanding is a highest-level operation in computer vision, and it is a very difficult and largely unsolved problem. The conventional methods usually use global features (such as color histogram, texture, edge) for image representation and recognize scene types with some classifiers (such as Bayesian, Neural Network, Support Vector Machine and so on). However, the recognition rate still cannot satisfy the requirement of real applications. In this paper, we proposed to use weighted fusion of global feature (Color histogram) and local feature (Bag-Of-Feature model) for scene image representation, and use hierarchical classifier according the visual feature properties of scene types for scene recognition. Experimental results show that the recognition rate with our proposed algorithm can be improved compared to the state of art algorithms.