Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features
This addresses the problem of unsupervised object discovery for computer vision applications, but it is incremental as it builds on existing pattern mining and pre-trained CNN features.
The paper tackles the problem of discovering dominant objects from a single unlabeled image, which is more challenging than typical localization tasks, and proposes Object Location Mining (OLM), achieving competitive localization performance compared to state-of-the-art methods on various benchmarks.
TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs). Specifically, we first convert the feature maps from a pre-trained CNN model into a set of transactions, and then discovers frequent patterns from transaction database through pattern mining techniques. We observe that those discovered patterns, i.e., co-occurrence highlighted regions, typically hold appearance and spatial consistency. Motivated by this observation, we can easily discover and localize possible objects by merging relevant meaningful patterns. Extensive experiments on a variety of benchmarks demonstrate that OLM achieves competitive localization performance compared with the state-of-the-art methods. We also evaluate our approach compared with unsupervised saliency detection methods and achieves competitive results on seven benchmark datasets. Moreover, we conduct experiments on fine-grained classification to show that our proposed method can locate the entire object and parts accurately, which can benefit to improving the classification results significantly.