Incremental models based on features persistence for object recognition

Object recognition has regained a high level of attention in recent years, with the application of deep convolutional neural networks to classification tasks. However, the problem of recognising objects for which a limited number of images is available is still open. In this paper, we propose a view-based object recognition method which can deal with objects represented by a handful of images. Salient points are extracted from the images, and a persistence value is defined for each point and updated as new images are added. An object model is built and refined on the basis of salient point persistence, where points with high persistence have priority over those with low persistence. The model can then be used to match a single image of an object. We demonstrate the efficacy of the proposed methodology on a dataset made of a collection of objects of cultural interest. We show that the recognition performance of the proposed method is superior to that of a competing methodology based on Bag-of-Words.