In order to solve the challenging problems of recognizing object in the angles changing and occlusion, a novel multi-angle algorithm is proposed by combining the Gabor feature and shared LIOP (Local Intensity Order Pattern) feature during the changing poses. First of all, the input image is filtered by a 2D Gabor filter in 4 directions and 16 scales to obtain 64 groups of characteristic response map. And then the scale and translation invariant feature can be derived from computing the maximum response value among the adjacent scales and position. Secondly, the geometric transformation algorithm is utilized to gain the shared LIOP feature under different perspectives. Thirdly, for reducing the time complexity, the dimension of combined features is reduced by the principal component analysis. At last, the calculated feature is trained and learned in SVM for the detecting model. Two standard test databases, Caltech 101 and UIUC car, are introduced to evaluate the accuracy and robustness of the proposed algorithm. The experimental results show that the average precision on two standard databases reach 92.1% and 95.4% high respectively, indicating the excellent performance of the proposed algorithm in recognizing object under various scales and angles.