Semantic Approach to Image Database Classification and Retrieval (「夏のデータベースワークショップ(DBWS2003)」一般)

スポンサーリンク

概要

• 論文の詳細を見る

• This paper demonstrates an approach to image retrieval founded on classifying image regions hierarchically based on their semantics (e.g. sky, snow, rocks, etc.) that resemble peoples' perception rather than on low-level features (e.g. color, texture, shape, etc.). Particularly, we consider outdoor images and automatically classify their regions based on their semantics using the support vector machines (SVMs) tool. First, image regions are segmented using the hill-climbing method. Then, those regions are classified by the SVMs. The SVMs learns the semantics of specified classes from a test database of image regions. Such semantic classification allows the implementation of intuitive query interface. As we show in our experiments, the high precision of semantic classification justifies the feasibility of our approach.

• 社団法人電子情報通信学会の論文
• 2003-07-11

著者

• Aghbari Zaher

  Graduate School Of Information Science And Electrical Engineering Kyushu University

• Makinouchi Akifumi

  Graduate School Of Information Science And Electrical Engineering Kyushu University

• OHASHI Takumi

  Graduate School of Information Science and Electrical Engineering, Kyushu University

• Ohashi Takumi

  Graduate School Of Information Science And Electrical Engineering Kyushu University

関連論文

• Semantic Approach to Image Database Classification and Retrieval (夏のワークショップDBWS2003)
• Semantic Approach to Image Database Classification and Retrieval (「夏のデータベースワークショップ(DBWS2003)」一般)

スポンサーリンク