Efficient Feature Detection and Effective Post-Verification for Large Scale Near-Duplicate Image Search

Abstract

State-of-the-art near-duplicate image search systems mostly build on the bag-of-local features (BOF) representation. While favorable for simplicity and scalability, these systems have three shortcomings: 1) high time complexity of the local feature detection; 2) discriminability reduction of local descriptors due to BOF quantization; and 3) neglect of the geometric relationships among local features after BOF representation. To overcome these shortcomings, we propose a novel framework by using graphics processing units (GPU). The main contributions of our method are: 1) a new fast local feature detector coined Harris-Hessian (H-H) is designed according to the characteristics of GPU to accelerate the local feature detection; 2) the spatial information around each local feature is incorporated to improve its discriminability, supplying semi-local spatial coherent verification (LSC); and 3) a new pairwise weak geometric consistency constraint (P-WGC) algorithm is proposed to refine the search result. Additionally, part of the system is implemented on GPU to improve efficiency. Experiments conducted on reference datasets and a dataset of one million images demonstrate the effectiveness and efficiency of H-H, LSC, and P-WGC.