基于WCDPM模型的细粒度物体识别

针对可变形部件模型(deformable parts model,DPM)同等对待各部件,无法体现不同部件对识别过程的贡献度差异的不足,提出一种权重系数可变形模型(weighted coefficient deformable parts model,WCDPM),对DPM中的各部件赋予权重,强调区分度较高的部件在识别过程的作用,弱化区分度低的部件对识别的影响,提高细粒度识别精度.同时给出了模型的训练过程和权重系数的学习方法.在Airplan OID和Oxford-IIIT Pet两个数据集上进行实验,验证了该方法的有效性.

Fine-grained Recognition Based on WCDPM Model

Since it treats the parts equally, while the deformable parts model ( DPM) cannot highlight distinctive parts that are helpful to distinguishing subtle categories. To cope with the problem mentioned above, a weighted coefficient deformable parts model ( WCDPM) was proposed to highlight distinctive parts and decrease the influence of non-distinctive parts, which leaded to improving performance in terms of fine-grained recognition accuracy. The detailed processes of model training and coefficient learning were also presented. Experimental results of Airplan OID and Oxford-IIIT Pet data sets demonstrate the effectiveness of the proposed method.