<会議発表論文>
User Fatigue Reduction by an Absolute Rating Data-trained Predictor in IEC
| 作成者 | |
|---|---|
| 本文言語 | |
| 出版者 | |
| 発行日 | |
| 収録物名 | |
| 巻 | |
| 開始ページ | |
| 終了ページ | |
| 出版タイプ | |
| アクセス権 | |
| 関連DOI | |
| 関連URI | |
| 関連情報 | |
| 概要 | Predicting IEC users' evaluation characteristics is one way of reducing users’ fatigue. However, users’ relative evaluation appears as noise to the algorithm which learns and predicts the users' evalu...ation characteristics. This paper introduces the idea of absolute scale to improve the performance of predicting users' subjective evaluation characteristics in IEC, and thus it will accelerate EC convergence and reduce users' fatigue. We first evaluate the effectiveness of the proposed method using seven benchmark functions instead of a human user. The experimental results show that the convergence speed of an IEC using the proposed absolute rating datatrained predictor is much faster than that of an IEC using a conventional predictor training with relative rating data. Next, the proposed algorithm is used in an individual emotion fashion image retrieval system. Experimental results of sign tests demonstrate that the proposed algorithm can alleviate user fatigue and has a good performance in individual emotional image retrieval.続きを見る |
| 目次 | Ⅰ.INTRODUCTION Ⅱ.IEC WITH ABSOLUTE RATING DATA-TRAINED PREDICTOR Ⅲ.SIMULATION EXPERIMENTS Ⅳ.SUBJECTIVE EXPERIMENTS ON EMOTION IMAGE RETRIEVAL Ⅴ.CONCLUSION |
本文ファイル
| ファイル | ファイルタイプ | 利用条件 | サイズ | 閲覧回数 | 説明 |
|---|---|---|---|---|---|
|
|
なし | 880 KB | 505 |
詳細
| レコードID | |
|---|---|
| 査読有無 | |
| ISSN | |
| ISBN | |
| DOI | |
| 注記 | |
| 登録日 | 2017.06.14 |
| 更新日 | 2021.10.06 |
Mendeley出力