Novel Global and Local Features for Near-Duplicate Document Image Matching
A new near-duplicate document image matching approach is proposed. Globally, we model the spatial arrangements of objects in an image. Locally, the micro-patterns within each object are captured. To define a micro-pattern, the N-nary center-symmetric gray value differences in an image local neighborhood of a variable radius are exploited. A visual descriptor is proposed to characterize the appearance of the object based on micro-pattern distributions. By combining the global and local features, each document image is represented by a compact signature with a variable length. We employ Earth Mover's Distance for image dissimilarity computation, which stands out for its remarkable ability to tolerate the instability of object segmentation by allowing many-to-many correspondence among objects. Extensive experiments on two data sets demonstrate the effectiveness of the proposed approach.