Histogram Based Corner Angle Representation for Object Retrieval

The paper presents an algorithm which uses corner and intersecting lines angle information with the corner boundary pixel information to construct a feature vector for representing an image and subsequently for searching the similar images from a database. The corners are extracted by finding the intersections of the detected lines found using Hough transform. Taking the corner as the center coordinate, the angles of the intersecting lines are determined and inner and outer object boundary pixel information around the corner is used to represent the corner geometry. For similarity measurement, the histogram of the information is used. This result in a significant small size feature matrix compared to the algorithms using color features. Experimental results show that it is computationally efficient in similarity measurement and the image corners being noise invariant produce good results in noisy environments.