为了实现对图像中多种类目标的检测，缩短目标搜索时间，本文基于图像目标的3个显著性线索(显著性检测，颜色对比，超像素跨越),构建了一种改进的通用无监督目标检测模型。通过机器学习center-surrounding比例参数，计算各个线索的显著度得分，并在朴素贝叶斯框架下对这3个目标显著性线索进行融合，以最终确定窗口中包含图像目标的概率。实验参数在PASCAL VOC 2007图像库进行检测，检测率为28.94%，击中率达96.99%; 在MSRC图片库进行检测，检测率为80.64%，击中率达99.10%; 得到的结果证明了本文模型的通用性。另外， 该模型对单幅图像的处理时间较Bogdan的检测模型提高了40%，改进了目标检测效率。本文模型可为后续的目标识别,图像分割提供更快、更准确的先验位置信息。

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.