DEEP CONDITIONAL NEURAL NETWORK FOR IMAGE SEGMENTATION

Existing joint models of deep Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs) face two problems for object segmentation: 1) CNNs can hardly extract high level features; 2) fully connected layers of CNNs are lack of capability of dealing with structured multi-level features. To address these problems, we utilize a Structured Random Forests based border ownership detection method to extract high level border features, which simulates the function of humans secondary visual cortex (V2). Moreover, an improved Conditional Boltzmann Machines (CBMs) are proposed to model predicted labels, local and global contexts of objects with multi-scale and multilevel features. Meanwhile, the proposed model inherits the merits of CNN, i.e., the good simulation of low level feature extraction ability in primary visual cortex (V1). Experiments demonstrate that our models yield competitive results on PASCAL VOC 2012 dataset.