抄録
While HMAX features have been proved to have excellent performance in image categorization tasks, the computational cost of recognition is expensive. If we aim to apply the HMAX features to object localization tasks, in which the categorization tasks are repeatedly performed by sliding windows, their processing time increases enormously. In this paper, we propose a method for speed up in computation of object localization based on HMAX features. We found that the HMAX features cause specific redundancies in the sliding window approach. The speed up is achieved by eliminating the redundancies in our method. The results from experiments using the University of Illinois-Urbana-Champaign (UIUC) car dataset and the face detection dataset benchmark (FDDB) indicate that this modification improved processing speeds significantly with insignificant reductions in precision.
本文言語 | English |
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ページ(範囲) | 60-73 |
ページ数 | 14 |
ジャーナル | ITE Transactions on Media Technology and Applications |
巻 | 2 |
号 | 1 |
DOI | |
出版ステータス | Published - 2014 |
ASJC Scopus subject areas
- 信号処理
- メディア記述
- コンピュータ グラフィックスおよびコンピュータ支援設計