Similar interior coordination image retrieval with multi-view features

Abstract

This paper presents a novel similar image retrieval method for interior coordination. Interior coordination is very familiar; however, it is still an abstract and difficult concept. Even if we are involved in coordination every day, it does not mean we can become professional coordinators. By realizing the retrieval that can provide similar interior coordination images from a query room image, inspiring users' ideas for interior coordination becomes feasible. In the proposed method, we extract image features specialized for interior coordination and realize similar interior coordination image retrieval. We employ multi-view features: object-based, color-based, and semantic-based features, in the feature extraction phase. The extracted features are used to calculate similarity between the query image and the database images for the retrieval. We conducted experiments using a sophisticated real-world interior coordination image dataset. Furthermore, we qualitatively and quantitatively evaluated the effectiveness of the proposed method.

Customer interest estimation method in real store using re-identification and 3D posture estimation models

“…In the industrial world, AI application to the manufacturing industry have actively made progress. Although there is a need for the introduction of AI technology to the retail industry, AI application in the retail industry has relatively been slow due to the characteristics of the obtained data 4 . In the retail industry, a source of competitiveness is the ability to provide products and services that meet the needs arising from various factors, such as trends, people's diverse hobbies, and interests.…”