Manifold learning of depth label for single image

The spatial position of the background and foreground determines the relative depth of the scene in the image. Using similarity characteristics of the local region of image and properties of dimensionality reduction of the manifold structure, the depth sortingindexing performance of the DCT high coefficientsfrequency distribution in the salient region was applied, the probability image map model of Markov Random Field(MRF) was defined to establish a relationship between the local feature and depth of different locations in the image. By segmenting the object, detecting relative blurring of the salient regions, and finally the relative depth map of the scene in the image was estimated. Through learning data embedding of manifold of the image, the probability density function of the data manifold distribution was migrated, the probability density function of category labels of object which followed similar manifold distributions was obtained. The blurred extent of salient regions was detected further, the high-frequency coefficient of discrete cosine transform(DCT) of multi-scale gradient amplitudes was fused, then depth mark index was calculated according to the high frequency characteristics of the fuzzy change to determine the hierarchical order of the depth tags, and the category tags were merged to generate a depth map. In this model framework, the blurred and unambiguous areas in a single image were detected to obtain the relative depth of the scene in the image, without knowing the priori settings of the camera or the type of blur. The depth estimation performance of the image was evaluated by using the MRF depth map model in a typical depth map estimation data set. The experimental results show the accuracy of the method in detecting scene distribution and ordering the depth of scene. It verifies the validity of the method.