An image cannot be segmented well using one feature in one subclass because the objects in an image region contain different subclasses. The sparse representation classifier (SRC)

a combined result of machine learning and compressed sensing

can solve such problems by constructing a dictionary that contains all the features of all subclasses. However

the performance of the SRC depends on an over-complete dictionary. Thus

the SRC needs a large number of training samples to obtain a good segmentation result. Therefore

we propose an interactive segmentation method based on similarity search and spatial constrain. First

each object dictionary that contains different features from corresponding subclasses is designed separately by manual labeling. The whole dictionary is then constructed by arranging the object dictionaries obtained by manual labeling in sequence. The most similar feature of the test sample is searched in each dictionary. The fitting term is built using the distance between the testing sample and the most similar feature in each subclass. Afterward

the object function is built by integrating the fitting term and the spatial constraint using total variation. With the addition of the spatial constraint

our model can reduce the effect of noise on the image segmentation results. The continuous max-flow algorithm is applied to minimize the objective function and to effectively obtain segmentation results. The continuous max-flow algorithm

which is linked to the continuous min-cut problem

is a novel method for solving the standard TV-based formulations of the Potts model. This algorithm can avoid extra computational load in enforcing simplex constraints and naturally allows parallel computations over different labels. Our method can segment different nature images with different object shapes or contents and is robust to the location and number of training samples. Compared with that of the SRC method

the efficiency of our method is relatively better when using the same dictionary. In addition

the segmentation results of our method are much better than those of traditional segmentation methods

such as N-cut and the logistic regression classifier. These results demonstrate that our method can segment a whole region of different objects in an image. The SRC can segment an image that contains different subclasses using an over-complete dictionary and a reconstruction strategy. However

the segmentation performance of the SRC is weak when only a few training samples are used. In this work

the similar feature fitting method and a spatial constraint are used to build a Potts model. The continuous max-flow is applied to solve the objective function and to obtain segmentation results. Our method offers the following advantages. 1) The feature fitting strategy is suitable for image segmentation with a small number of training samples

and it can solve the problem where image objects contain different subclasses. 2) The spatial constraint based on total variation can avoid the noise effect during image segmentation and improve the accuracy of the segmentation results. 3) The continuous max-flow applied to solve our objective function can avoid extra computational load in enforcing simplex constraints and naturally allows parallel computations over different labels. Comparative experiments with SRC

N-cut

and LRC methods demonstrate that our method can segment a whole region with different objects in an image.