To measure a variety of objects of an image and to reduce the detection time, an unsupervised object detection model was established to provide location priors. The model was mainly based on three image cues of a object, and they are saliency detection , color contrast and superpixel straddling. To determine the likelihood of image object contained in a window, the saliency scores of the three cues were calculated, and the saliency cues of the three objects were fused in a simple Bayesian framework by a machine learning center-surrounding proportion parameter. In experiments on the challenging PASCAL VOC 07 dataset, it shows that the detection rate is 28.94 % ,the hit rate is 96.99% and the combined measuring result is better than any cue alone. In experiments on MSRC dataset, it shows that the proposed model is generic and efficient ,the detection rate is 80.64 %, the hit rate is 99.10% and the average processing time is 40% less than that of Bogdan's model.These results from extensive field tests suggest that proposed model can provide better location priors to the object recognition and image segmentation where the location of object is unknown.