ARTRIEVAL: Painting Retrieval Without Expert Knowledge

Abstract

As people are becoming interested in paintings, various userinteractive search systems have been presented in recent times. Many systems encourage users to search paintings by prior knowledge on paintings. We discover the limitation for existing methods on how well the query is represented by the user, and propose a simple, yet effective way to search the painting by exploiting the color to express human visual memory. To achieve our goal, we suggest color clustering based on human color perception, and hierarchical metric learning to accommodate the locality of colors. With userinteractive drawing through learned colors, the user completes the abstract image to resemble the visual memory. We show that our system is easy to use, fast to process, accurate to search and fully extensible to cover deviation among users.

Publication
IEEE Conference on Image Processing (ICIP)
painting retrieval interactive search color clustering metric learning