The number of digital images is growing extremely rapidly, and so is the need for their classification. But, as more images of pre-defined categories become available, they also become more diverse and cover finer semantic differences. Ultimately, the categories themselves need to be divided into subcategories to account for that semantic refinement. Image classification in general has improved significantly over the last few years, but it still requires a massive amount of manually annotated data. Subdividing categories into subcategories multiples the number of labels, aggravating the annotation problem. Hence, we can expect the annotations to be refined only for a subset of the already labeled data, and exploit coarser labeled data to improve classification. In this work, we investigate how coarse category labels can be used to improve the classification of subcategories. To this end, we adopt the framework of Random Forests and propose a regularized objective function that takes into account relations between categories and subcategories. Compared to approaches that disregard the extra coarse labeled data, we achieve a relative improvement in subcategory classification accuracy of up to 22% in our large-scale image classification experiments.
Ristin et al. REF adopt the framework of Random Forests and proposed a regularized objective function that takes into account relations between categories and subcategories to improve the classification accuracy.
From categories to subcategories: Large-scale image classification with partial class label refinement
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science