This paper presents a novel approach to recognize a scene presented in an image with specific application to scene classification in field sports video. We propose different variants of the algorithm ranging from bags of visual words to the simplified real-time implementation, that takes only the most important areas of similar colour into account. All the variants feature similar accuracy which is comparable to very well-known image indexing techniques like SIFT or HoGs. For the comparison purposes, we also developed a specific database which is now available online. The algorithm is suitable in scene recognition task thanks to changes in speed and robustness to the image resolution, thus, making it a good candidate in real-time video indexing systems. The procedure features high simplicity thanks to the fact that it is based on the very well-known Fourier transform.

中文翻译：

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使用颜色和频率空间分解的实时野外运动场景分类。

本文提出了一种新颖的方法来识别图像中呈现的场景，并将其特定地应用于野外运动视频中的场景分类。我们提出了该算法的不同变体，从视觉单词袋到简化的实时实现，仅考虑相似颜色的最重要区域。所有变体均具有相似的精度，可与非常著名的图像索引技术（如SIFT或HoG）相媲美。为了进行比较，我们还开发了一个特定的数据库，现在可以在线访问该数据库。由于速度和图像分辨率的鲁棒性变化，该算法适用于场景识别任务，因此使其成为实时视频索引系统的理想选择。