The invention discloses a multi-classifier integration method based on maximum expected parameter estimation, mainly relating to a relative feedback-based new image search method for integrating a plurality of single classifiers by utilizing a maximum expected parameter estimation method. In the method, an extraction unit, a search unit, a marking unit and a studying unit are provided, the method comprises the following specific steps: firstly extracting low-level visual features of each image, such as color, texture, shape and the like; randomly selecting an image from an image library by a user, comparing the similarity of the image feature with that of the low-level feature of all images in the image library by using a Euclidean distance algorithm, ordering the similarity according to size and returning the first 10 images to the user; and judging whether the returned images and the previously selected images are in the same semantic group, if so, marking the returned images as images of positive instance and images of negative instance, putting the marked images into a support vector machine to train, and then feeding back the learned result to the user.