Combining Interest Points and Edges for Building and Object Recognition

We present a novel approach using combined features to recognize images containing specific objects and buildings.The content of an image is characterized by two kinds of features：Harris-Laplace interest points described by the scale invariant feature transform （SIFT） descriptor and edges described by the edge color histogram.Edges and corners contain the maximal amount of information necessary for object recognition.The feature detection in this work is an integrated process：edges are detected directly based on the Harris function；Harris interest points are detected at several scales and Harris-Laplace interest points are found using the Laplace function.The combination of edges and interest points brings efficient feature detection and high recognition ratio to the object recognition system.Experimental results show that this system has good performance.