Scene recognition plays an important role in the task of visual information retrieval, segmentation and image/video understanding. Traditional approaches for scene recognition usually utilize handcrafted features and have the drawbacks of poor representation ability, which can be improved by employing deep convolutional neural network (CNN) features that contain more semantic and structure information and thus possess more discriminative ability via multiple linear and non-linear transformations. However, an amount of detailed information may be lost when only the final output features which have gone through a certain number of transformations are applied to scene recognition. The features which are generated from the intermediate layers are not fully utilized. In this work, the GoogLeNet model is employed and divided into three parts of layers from bottom to top. The output features from each of the three parts are applied for scene recognition, which leads to the proposed GoogLeNet based multi-stage feature fusion (G-MS2F). What's more, the product rule is used to generate the final decision for scene recognition from the three outputs corresponding to the three parts of the proposed model. The experimental results demonstrate that the proposed model is superior to a number of state-of-the-art CNN models for scene recognition, and obtains the recognition accuracy of 92.90%, 79.63% and 64.06% on the benchmark scene recognition datasets Scene15, MIT67 and SUN397, respectively.
Bibliographical noteThis work was supported in part by the National Natural Science Foundation of China under Grants 61472281 and 61622115, and the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning (No. GZ2015005).
- Convolutional neural network
- Feature fusion
- Multi-stage feature
- Scene recognition