〇吉野遼, 劉海龍, 高野敏明, 谷口忠大, Deep Sparse Auto Encoderを用いた画像特徴抽出に基づくカテゴリ形成に関する研究, 第58回システム制御情報学会研究発表講演会 (SCI14), CD-216-6, (2014.5.21-23@京都)

发布日期：May 30, 2014 6:29:42 AM

Abstract:

In this article, Deep Sparse Autoencoder(DSAE) which is one of the techniques of deep learning is evaluated as a feature extractor. To evaluate the DSAE，the extracted features obtained by the proposal technique and by Scale-invariant feature transform(SIFT) were classified using a latent dirichlet allocation(LDA). The performance of the feature extractor was compared from the view point of classification performance. As a result, DSAE classified an object individually.