整个对象信息对场景识别有帮助吗?

Hongje Seong, Junhyuk Hyun, Euntai Kim
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摘要

场景识别是一种视觉任务,对图像上的地点类别进行分类。场景图像可能包含各种各样的物体,这些物体往往成为识别图像中场景的线索。因此,以前的许多场景识别方法都是利用图像中出现的物体信息来提高性能。在这里,我们提出了一个问题,即整个物体信息是否有助于场景识别。为了找到这个问题的答案,我们在Places365上进行了实验,这是由真实世界图像组成的最大的场景识别数据集。为了找到干扰场景识别的对象类别,我们使用了类转换矩阵,这是一种深度学习方法。最后,我们发现一些对象类可能有助于干扰场景识别。这表明,不仅要充分利用目标信息,而且要消除干扰的目标信息。
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Is Whole Object Information Helpful for Scene Recognition?
Scene recognition is one of the visual tasks, classifying a place category on an image. Scene images may contain various objects, and these objects tend to become clues to recognize the scene of the image. Therefore, many previous approaches for scene recognition use the object information that appeared in the image to improve the performance. Here, we raise a question of whether whole object information is helpful for scene recognition. To find the answer to the question, we conduct experiments on Places365, which is the largest scene recognition dataset consist of real-world images. To find the object classes which disturbed scene recognition, we utilize the Class Conversion Matrix, which is a deep learning approach. Finally, we found that some object classes may contribute to disturbing scene recognition. It indicates that not only making good use of object information, but also dropping disturbed object information is also important.
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