On the basis of a visual attention model and a maximum entropy segmentation method, an adaptive segmentation method was proposed to segment the object from a complex background in the scene image and to detect a salient object effectively and accurately. First, the feature of original image was extracted via four channels on color, intensity, orientation and local energy. The profile of object feature was described more accurately by combining the channel of local energy with a simple biologically-inspired model. Then, object detection masks were constructed to remove background gradually according to the gray intensity of the pixels in the saliency map. By taking blend masks with the original image as a pre-segmentation result, the entropy of pre-segmentation images was computed. Finally, the entropy of salient object was estimated via maximization information entropy principle and the optimized image extraction for the salient object was obtained by estimating the relationship of entropy between salient object and masks in the saliency map. Experimental results indicate that the salient object detected by proposed method is more integrity, the F-measure of segmentation performance is 0.56, and the precision ratio and the recall ratio of detection are 0.69 and 0.41, respectively. The proposed method is more reasonable and effective than the traditional method, and it can satisfy the requirements of detecting the salient objects from complex backgrounds.