Cascaded sparse color-localized matching for logo retrieval

In this paper we present a framework for logo retrieval in natural images. Color-localized spatial masks are used as an alternative to computationally expensive spatial verification techniques like RANSAC. First, keypoints are detected using traditional techniques such as the SIFT detector. Local masks are defined around each keypoint that take its scale and orientation information into account. To exploit inherent color information presented in brand logos, ordered color histograms are extracted from masked regions. A separate vocabulary is constructed for both SIFT descriptors (visual word) and color histograms (color word). For faster matching during runtime, a two-stage cascaded index is designed, which maps the visual word and color word tuple to a list of relevant images. This list is finally re-ranked with BoW cosine similarity to generate relevant matches for the input query. To demonstrate the efficacy of our method, we conduct experiments on two popular logo datasets: Flickr27 and Flickr32. Our experimental results illustrate State-of-the-art retrieval performance on these datasets with potential for added speed and a lower memory footprint as indicated by the low response ratio.