Euclidean Image Embedding in view of Similarity Ranking in Auction Search by Image

We propose an Euclidean embedding image representation, which serves to rank auction item images through wide range of semantic similarity spectrum, in the order of the relevance to the given query image much more effective than the baseline method in terms of a graded relevance measure. Our method uses three stream deep convolutional siamese networks to learn a distance metric and we leverage search query logs of an auction item search of the largest auction service in Japan. Unlike previous approaches, we define the inter-image relevance on the basis of user queries in the logs used to search each auction item, which enables us to acquire the image representation preserving the features concerning user intents in real e-commerce world.