In order to effectively solve the multiple-instance image classification problem

we put forward a new classification method

which transforms the multiple-instance image into a single instance image in the new space-bag space. First

the whole image is regarded as a bag and each region as an instance of that bag. According to the same visual regions of image samples are put into one cluster and k-means clustering algorithm is used to determine the visual words for each class of images. At this step

we use the information that labels of negative samples are all known has been used to select the typical visual words. Then

we construct a new bag space with these visual words and use a nonlinear function based on these visual words to transform each multiple-instance image into a point in the bag space. Finally

standard SVMs are trained in the bag feature space to classify the images. Experimental results and comparisons on the Corel image set are given to illustrate the performance of the new method.