Abstract
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.
Highlights
Recent developments in deep learning techniques has enabled us to accurately recognize the concepts captured in images based on visual appearances
Based on the ideas discussed above, this paper proposes a method for constructing a geospatial concept graph, which represents a structured knowledge about geospatial concepts necessary for geo-aware fine-grained image recognition, by utilizing tagged images shared on Flickr
The objective of this work is to increase the diversity of fine-grained geospatial concepts to which geo-aware fine-grained image recognition can be applied
Summary
Recent developments in deep learning techniques has enabled us to accurately recognize the concepts captured in images based on visual appearances. While many approaches have been proposed for discriminating their subtle visual differences by focusing on local parts in the images or by learning discriminative visual feature representation, others leverage the additional information such as geographic locations where the images were captured, so that the visually similar concepts are distinguished based on their captured locations [1]. Such geo-aware fine-grained image recognition is possible for the concepts whose subordinate concepts are likely to be observed at different locations. While the manually created datasets for a predetermined set of fine-grained geospatial concepts enable us to improve the recognition performance, the domains of recognizable concepts are limited due to the availability of such datasets
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