While visual appearances play a main role in recognizing the concepts captured in images, additional information can provide complementary information for fine-grained image recognition, where concepts with similar visual appearances such as species of birds need to be distinguished. Especially for recognizing geospatial concepts, which are observed only at specific places, geographical locations of the images can improve the recognition accuracy. However, such geo-aware fine-grained image recognition requires prior information about the visual and geospatial features of each concept or the training data composed of high-quality images for each concept associated with correct geographical locations. By using a large number of images photographed in various places and described with textual tags which can be collected from image sharing services such as Flickr, this paper proposes a method for constructing a geospatial concept graph which contains the necessary prior information for realizing the geo-aware fine-grained image recognition, such as a set of visually recognizable fine-grained geospatial concepts, their visual and geospatial features, and the coarse-grained representative visual concepts whose visual features can be transferred to several fine-grained geospatial concepts. Leveraging the information from the images captured by many people can automatically extract diverse types of geospatial concepts with proper features for realizing efficient and effective geo-aware fine-grained image recognition.

中文翻译：

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从标记图像构建地理空间概念图以识别地理感知的细粒度图像

视觉外观在识别图像中捕获的概念中起主要作用，而其他信息则可以为细粒度图像识别提供补充信息，其中需要区分具有相似视觉外观的概念（例如鸟类）。特别是用于识别地理空间概念，仅在特定位置观察到的图像的地理位置可以提高识别精度。但是，这种具有地理意识的细粒度图像识别需要有关每个概念的视觉和地理空间特征的先验信息，或者是由与正确地理位置相关联的每个概念的高质量图像组成的训练数据。通过使用在不同地方拍摄的大量图像，并使用可从Flickr等图像共享服务收集的文本标签进行描述，本文提出了一种构建地理空间概念图的方法，该图包含实现地理信息的必要先验信息。感知的细粒度图像识别，例如一组视觉上可识别的细粒度地理空间概念，它们的视觉和地理空间特征，以及具有粗粒度代表性的视觉概念，其视觉特征可以转换为多个细粒度的地理空间概念。利用许多人捕获的图像中的信息，可以自动提取具有适当功能的多种类型的地理空间概念，以实现高效，有效的地理感知细粒度图像识别。