Tree-based Shape Descriptor for scalable logo detection

Abstract

Detecting logos in real-world images is a great challenging task due to a variety of viewpoint or light condition changes and real-time requirements in practice. Conventional object detection methods, e.g., part-based model, may suffer from expensively computational cost if it was directly applied to this task. A promising alternative, triangle structural descriptor associated with matching strategy, offers an efficient way of recognizing logos. However, the descriptor fails to the rotation of logo images that often occurs when viewpoint changes. To overcome this shortcoming, we propose a new Tree-based Shape Descriptor (TSD) in this paper, which is strictly invariant to affine transformation in real-world images. The core of proposed descriptor is to encode the shape of logos by depicting both appearance and spatial information of four local key-points. In the training stage, an efficient algorithm is introduced to mine a discriminate subset of four tuples from all possible key-point combinations. Moreover, a root indexing scheme is designed to enable to detect multiple logos simultaneously. Extensive experiments on three benchmarks demonstrate the superiority of proposed approach over state-of-the-art methods.