Improving Object Classification Performance via Confusing Categories Study
Shangwen Li, Chen Chen, Yuzhuo Ren, C.‐C. Jay Kuo · 2018
As the number of object categories continues to increase in object classification, it is inevitable to have certain categories that are more confusing than others due to the proximity of their samples in the feature space. In this work, we conduct a detail analysis on confusing categories and propose a confusing categories identification and resolution (CCIR) scheme, which can be applied to any CNNbased object classification baseline method to further improve its performance. In the CCIR scheme, we first present a procedure to cluster confusing object categories together to form a confusion set automatically. Then, a binary-treestructured (BTS) clustering method is adopted to split a confusion set into multiple subsets. A classifier is subsequently learned within each subset to enhance its performance. Experimental results on the ImageNet ILSVRC2012 dataset show that the proposed CCIR scheme can offer a significant performance gain over the AlexNet and the VGG16.