Unsupervised object discovery for instance recognition
This addresses a challenge in computer vision for tasks like image retrieval, but it is incremental as it builds on existing methods for handling clutter.
The paper tackles the problem of background clutter corrupting global descriptors in large-scale image retrieval by proposing an unsupervised salient region detection method, which improves object retrieval performance, especially for small objects in large collections.
Severe background clutter is challenging in many computer vision tasks, including large-scale image retrieval. Global descriptors, that are popular due to their memory and search efficiency, are especially prone to corruption by such a clutter. Eliminating the impact of the clutter on the image descriptor increases the chance of retrieving relevant images and prevents topic drift due to actually retrieving the clutter in the case of query expansion. In this work, we propose a novel salient region detection method. It captures, in an unsupervised manner, patterns that are both discriminative and common in the dataset. Saliency is based on a centrality measure of a nearest neighbor graph constructed from regional CNN representations of dataset images. The descriptors derived from the salient regions improve particular object retrieval, most noticeably in a large collections containing small objects.