In this work, we propose a heterogeneous committee (ensemble) of diverse members (classification approaches) to solve the problem of human epithelial (HEp-2) cell image classification using indirect Immunofluorescence (IIF) imaging. We hypothesize that an ensemble involving different feature representations can enable higher performance if individual members in the ensemble are sufficiently varied. These members are of two types: (1) CNN-based members, (2) traditional members. For the CNN members, we have employed the well-established ResNet, DenseNet, and Inception models, which have distinctive salient aspects. For the traditional members, we incorporate class-specific features which are characterized depending on visual morphological attributes, and some standard texture features. To select the members which are discriminating and not redundant, we use an information theoretic measure which considers the trade-off between individual accuracies and diversity among the members. For all selected members, a compelling fusion required to combine their outputs to reach a final decision. Thus, we also investigate various fusion methods that combine the opinion of the committee at different levels: maximum voting, product, decision template, Bayes, Dempster-Shafer, etc. The proposed method is evaluated using ICPR-2014 data which consists of more images than some previous datasets ICPR-2012 and demonstrate state-of-the-art performance. To check the effectiveness of the proposed methodology for other related datasets, we test our methodology with newly compiled large-scale HEp-2 dataset with 63K cell images and demonstrate comparable performance even with less number of training samples. The proposed method produces 99.80% and 86.03% accuracy respectively when tested on ICPR-2014 and a new large-scale data containing 63K samples.

在这项工作中，我们提出了一个由不同成员（分类方法）组成的异质委员会（集合），以解决使用间接免疫荧光 (IIF) 成像的人类上皮 (HEp-2) 细胞图像分类问题。我们假设，如果集成中的各个成员充分变化，则涉及不同特征表示的集成可以实现更高的性能。这些成员有两种类型：（1）基于 CNN 的成员，（2）传统成员。对于 CNN 成员，我们采用了完善的 ResNet、DenseNet 和 Inception 模型，它们具有独特的突出方面。对于传统成员，我们结合了特定于类的特征，这些特征取决于视觉形态属性和一些标准纹理特征。选择具有歧视性且不冗余的成员，我们使用一种信息理论衡量标准，该衡量标准考虑了个体准确性和成员多样性之间的权衡。对于所有选定的成员，需要结合他们的输出以达成最终决定的引人注目的融合。因此，我们还研究了结合不同级别委员会意见的各种融合方法：最大投票、产品、决策模板、贝叶斯、Dempster-Shafer 等。 使用由更多图像组成的 ICPR-2014 数据评估所提出的方法与之前的一些数据集 ICPR-2012 相比，并展示了最先进的性能。为了检查所提出的方法对其他相关数据集的有效性，我们使用新编译的具有 63K 细胞图像的大规模 HEp-2 数据集测试我们的方法，并证明即使训练样本数量较少也具有可比性。