控制与决策
控製與決策
공제여결책
CONTROL AND DECISION
2013年
4期
481-488
,共8页
多示例学习%图像分类%图像检索%图像语义分析
多示例學習%圖像分類%圖像檢索%圖像語義分析
다시례학습%도상분류%도상검색%도상어의분석
multi-instance learning%image categorization%image retrieval%image semantic analysis
多示例学习(MIL)作为第4种机器学习框架,已在图像语义分析中得到了广泛应用.首先介绍MIL的起源、特点、相关概念和数据集;然后以图像语义分析为应用背景,对相关MIL算法进行详细综述,按照算法采用的学习机制对其进行分类,并重点分析了各类算法提出的思路和主要特点;最后,对MIL未来的研究方向作了探讨.
多示例學習(MIL)作為第4種機器學習框架,已在圖像語義分析中得到瞭廣汎應用.首先介紹MIL的起源、特點、相關概唸和數據集;然後以圖像語義分析為應用揹景,對相關MIL算法進行詳細綜述,按照算法採用的學習機製對其進行分類,併重點分析瞭各類算法提齣的思路和主要特點;最後,對MIL未來的研究方嚮作瞭探討.
다시례학습(MIL)작위제4충궤기학습광가,이재도상어의분석중득도료엄범응용.수선개소MIL적기원、특점、상관개념화수거집;연후이도상어의분석위응용배경,대상관MIL산법진행상세종술,안조산법채용적학습궤제대기진행분류,병중점분석료각류산법제출적사로화주요특점;최후,대MIL미래적연구방향작료탐토.
@@@@Multi-instance learning(MIL) has been recognized as the fourth machine learning framework, and has been widely used in the image semantic analysis. Firstly, the concepts such as development history, characteristics and many useful testing datasets of MIL techniques are reviewed. Then, many popular MIL algorithms are also introduced in detail by using real-world applications based on image semantic analysis. Meanwhile, based on their machine learning mechanisms, related MIL algorithms are divided into a variety of categories, which highlights the processes and dominant features of different MIL algorithms. Finally, the trends and possible outputs for further researches are discussed in details.
