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Semi-Supervised Exploration in Image Retrieval

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arxiv 1906.04944 v1 pith:CEMUZV3K submitted 2019-06-12 cs.CV

Cheng Chang , Himanshu Rai , Satya Krishna Gorti , Junwei Ma , Chundi Liu , Guangwei Yu , Maksims Volkovs This is my paper · ORCID
classification cs.CV
keywords graphimagechallengelandmarkretrievalretrievesemi-supervisedsolution
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We present our solution to Landmark Image Retrieval Challenge 2019. This challenge was based on the large Google Landmarks Dataset V2[9]. The goal was to retrieve all database images containing the same landmark for every provided query image. Our solution is a combination of global and local models to form an initial KNN graph. We then use a novel extension of the recently proposed graph traversal method EGT [1] referred to as semi-supervised EGT to refine the graph and retrieve better candidates.

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