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中文摘要：
基于部件的目标检测模型主要研究如何利用部件获得目标的局部判别特征，而极少关注部件形式及选取策略对检测性能的影响．首先从特征学习的角度分析了部件选取策略对学习弱部件模型的影响，提出了一种自适应的部件学习方法．该方法无须部件层标注，在搜索判别部件的同时利用样本自身的图像分布特点自动定位到语义相关的部件，从而保证特征学习的判别性和有效性．其次，针对训练集的标注样本经常存在不完整或部分遮挡等事实，提出了一种简单有效的部件剪技策略以降低噪声部件的比例．实验面向PASCAI。VOC2007]2010数据集评估了4种形式的部件模型．实验结果验证了自适应部件学习算法在模型检测上的有效性，同时表明了弱部件模型经过剪技策略优化后具有更快的学习收敛性．
英文摘要：
Previous work on part-based models for object detection has concentrated on searching locally discriminative features representing objects based on notion of parts. There is little research on how to select parts effectively and what kind of parts could improve the object detections. This paper investigates the learning problem of object parts with weakly labeled data, and proposes an adaptive approach for part selection. Without part-level supervision, for each training example this approach first detects seed windows of parts using single-part classifiers and then localizes parts in local regions via the image-specific distribution. The selected parts, which contain discriminative and relevant features, are used to train global parameters. Addressing the partial object occlusions in training examples, a pruning strategy is introduced to reduce the proportion of noise parts during learning iterations. The experimental results on PASCAL VOC 2007 and 2010 datasets demonstrate that the proposed part learning method gets an improvement on object detections compared with three classical part models, and the pruning strategy can speed up the convergence rates of model learning.
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