This work aims for image categorization using a representation of distinctive parts. Different from existing part-based work, we argue that parts are naturally shared between image categories and should be modeled as such. We motivate our approach with a quantitative and qualitative analysis by backtracking where selected parts come from. Our analysis shows that in addition to the category parts defining the class, the parts coming from the background context and parts from other image categories improve categorization performance. Part selection should not be done separately for each category, but instead be shared and optimized over all categories. To incorporate part sharing between categories, we present an algorithm based on AdaBoost to jointly optimize part sharing and selection, as well as fusion with the global image representation. We achieve results competitive to the state-of-the-art on object, scene, and action categories, further improving over deep convolutional neural networks.

该研究旨在使用独特部分的表征方法对图像进行分类，通过对选定部分的回溯分析表明，除了类别部分以外，来自背景上下文和其他图像类别的部分也可以提高分类性能。为了实现部分在所有类别之间的共享和优化，提出了一种基于AdaBoost的算法来共同优化部分共享、选择以及与全局图像表征的融合。实验表明该方法具有与深度卷积神经网络相媲美的分类效果。

无备件：共享零件探测器进行图像分类