Lattice-Support repetitive local feature detection for visual search

Abstract

Repetitive patterns such as building facades, floor tiles, vegetation, and wallpapers are commonly found in sceneries and images. The presence of such repetitive patterns in images often leads to visual burstiness and geometric ambiguity, which poses challenge for state-of-the-art visual search technologies. To alleviate these problems, we propose a new lattice-support repetitive local feature detection method to detect repetitive patterns, estimate the underlying lattice structure, and enhance descriptors used for subsequent visual image search. Existing methods for repetitive pattern detection are commonly based on determining the underlying lattice structures. However, these structures do not correspond directly to robust features that are scale- and rotation-invariant. This paper proposes a new lattice-support repetitive local feature (LS-RLF) detection method that aims to integrate lattice information into repeated local feature detection and extraction. The advantage of the proposed method is that the detected features can be directly used by current visual search technologies. The LS-RLF method estimates the undetected repeated features in the lattice structure using Hough transform-based feature estimation. Further, in order to handle the visual burstiness issue, a new LS-RLF based image retrieval framework is developed. Experiments performed on benchmark datasets show that the proposed method outperforms the state-of-the-art methods by mean Average Precisions (mAP) of 4.5%, 5.5% and 3.2% on Oxford, Paris, and INRIA holidays datasets respectively. This demonstrates the effectiveness of the proposed method in performing visual search for images which contain wide range of repeated patterns.