Affine invariant visual phrases for object instance recognition

Abstract

Object instance recognition approaches based on the bag-of-words model are severely affected by the loss of spatial consistency during retrieval. As a result, costly RANSAC verification is needed to ensure geometric consistency between the query and the retrieved images. A common alternative is to inject geometric information directly into the retrieval procedure, by endowing the visual words with additional information. Most of the existing approaches in this category can efficiently handle only restricted classes of geometric transformations, including scale and translation. In this paper, we propose a simple and efficient scheme that can cover the more complex class of full affine transformations. We demonstrate the usefulness of our approach in the case of planar object instance recognition, such as recognition of books, logos, traffic signs, etc.

Affine invariant fusion feature extraction based on geometry descriptor and BIT for object recognition

“…Object recognition is achieved by a classifier such as support vector machine (SVM) [3–5] and convolutional neural network (CNN) in machine learning [6, 7]. Another approach is to extract features with affine invariance in feature space, and then a classifier is constructed for recognition such as geometric descriptors and BIT [8–10].…”