Boosting global scene classification accuracy by discriminative region localization

Combining global scene classification with object detection has helped in improving the classification accuracy. However, training an object detector requires a large amount of manual annotation. The object detector may also fail when the object is occluded. Meanwhile, the presence of the object is not only indicated by the entire object region but any of its parts or its correlations with other regions in the image. To overcome these limitations, we propose using discriminative region localization instead of object detection in the combination. Our contribution is two-fold, a) a complete framework that combines global scene classification with discriminative region localization for image classification and b) a weakly supervised discriminative region localization approach that utilizes spatial context to improve the learning accuracy. Our experimental results on benchmark datasets demonstrated that the proposed discriminative region localization approach outperforms the state-of-the-art approach. In addition, the combination significantly increases the classification performance.