A Novel Approach for Trademark Image Retrieval by Combining Global Features and Local Features

Traditional trademark image retrieval algorithm only using global feature easily makes mistaken retrieval, and scale invariant feature transform (SIFT) features have limited descriptive ability for image contour and high algorithm complexity. This paper proposes a trademark retrieval algorithm combining the image global features and local features. In this paper, we will firstly, extract Zernike moments of the retrieved image and sort them according to similarity. Candidate images are formed. Then, the SIFT features are used for matching the query image accurately with candidate images. Experimental results show that this method not only keeps high precisionrecall of SIFT features and is superior than the method based on the single Zernike moments feature, but also improves effective retrieval speed compared to the single SIFT features. This method can be well applied to the trademark image retrieval system.