Aiming at the object recognition in complex background,a recognition method,which simultaneously detects object location and classifies object types,is proposed.In this method,first,a large number of interest points are selected from the image and are filtered to obtain exactly-fitted outliers via the epipolar geometric constraint between images.Then,feature codebooks in the interest point space are formed via the local type consistent k-means clustering.Finally,for the given test interest points,the local features are voted to represent the object type,and the foreground object is obtained by maximizing the joint probability of object type and location.Experimental results on standard dataset Caltech-101 and real scene images demonstrate that the proposed method improves the recognition accuracy by about 8%.