Fine-Grained Image Classification via Spatial Saliency Extraction

Juntan Zhang, Feng-Wen Sun, Jie Song, Adam Von Ancken, Richard Zhai

Abstract: As an important type of image classification task, fine-grained image classification is of great practical interest. The major challenges of fine-grained image classification come from (1) The available training datasets are lack of object localization annotations which are highly time-consuming (2) The object discriminating a class generally only takes a very small portion of a whole image. In this paper, a spatial saliency extraction (SSE) approach is proposed to crop the possible attention areas inside a whole image to improve the fine-grained image classification performance. The proposed algorithm has been applied to the recently released Chest-X-ray14 dataset [1], which is highly imbalanced and weakly annotated. The SSE method offers the state-of-the-art classification performance.