This paper presents a practical optimization procedure for object detection and recognition algorithms. It is suitable for object recognition using a catadioptric omnidirectional vision system mounted on a mobile robot. We use the SIFT descriptor to obtain image features of the objects and the environment. First, sample object images are given for training and optimization procedures. Bayesian classification is used to train various test objects based on different SIFT vectors. The system selects the features based on the k-means group to predict the possible object from the candidate regions of the images. It is thus able to detect the object with arbitrary shape without the 3D information. The feature optimization procedure makes the object features more stable for recognition and classification. Experimental results are presented for real scene images captured by a catadioptric omni-vision camera.