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Scene Classification Based on Multi-resolution Orientation Histogram of Gabor Features

机译:基于Gabor特征多分辨率方向直方图的场景分类

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This paper presents a scene classification method based on multi-resolution orientation histogram. In recent years, some scene classification methods have been proposed because scene category information is used as the context for object detection and recognition. Recent studies uses the local parts without topological information. However, the middle size features with rough topolog-ical information are more effective for scene classification. For this purpose, we use orientation histogram with rough topological information. Since we do not the appropriate subregion size for computing orientation histogram, various sub-region sizes are prepared, and multi-resolution orientation histogram is developed. Support Vector Machine is used to classify the scene category. To improve the accuracy, the similarity between orientation histogram on the same subregion is used effectively. The proposed method is evaluated with the same database and protocol as the recent studies. We confirm that the proposed method outperforms the recent scene classification methods.
机译:提出了一种基于多分辨率方向直方图的场景分类方法。近年来,由于场景类别信息被用作对象检测和识别的上下文,因此提出了一些场景分类方法。最近的研究使用没有拓扑信息的局部。但是,具有大致拓扑信息的中等大小特征对于场景分类更为有效。为此,我们将方向直方图与大致的拓扑信息结合使用。由于我们没有合适的子区域大小来计算方向直方图,因此准备了各种子区域大小,并开发了多分辨率方向直方图。支持向量机用于对场景类别进行分类。为了提高精度,有效地利用了相同子区域上的方向直方图之间的相似性。使用与最新研究相同的数据库和协议对提出的方法进行评估。我们确认,提出的方法优于最近的场景分类方法。

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