CRF Learning with CNN Features for Image Segmentation (1503.08263v1)

Abstract: Conditional Random Rields (CRF) have been widely applied in image segmentations. While most studies rely on hand-crafted features, we here propose to exploit a pre-trained large convolutional neural network (CNN) to generate deep features for CRF learning. The deep CNN is trained on the ImageNet dataset and transferred to image segmentations here for constructing potentials of superpixels. Then the CRF parameters are learnt using a structured support vector machine (SSVM). To fully exploit context information in inference, we construct spatially related co-occurrence pairwise potentials and incorporate them into the energy function. This prefers labelling of object pairs that frequently co-occur in a certain spatial layout and at the same time avoids implausible labellings during the inference. Extensive experiments on binary and multi-class segmentation benchmarks demonstrate the promise of the proposed method. We thus provide new baselines for the segmentation performance on the Weizmann horse, Graz-02, MSRC-21, Stanford Background and PASCAL VOC 2011 datasets.

• The paper introduces a hybrid model that integrates CNN feature extraction into CRF frameworks, significantly improving segmentation accuracy.
• It leverages a large-margin SSVM for parameter learning and employs spatially related pairwise potentials to capture contextual relationships in images.
• Empirical results on multiple datasets show that CNN features outperform traditional handcrafted descriptors in both binary and multi-class segmentation tasks.

CRF Learning with CNN Features for Image Segmentation

The paper "CRF Learning with CNN Features for Image Segmentation" by Fayao Liu, Guosheng Lin, and Chunhua Shen explores the integration of Conditional Random Fields (CRFs) and Convolutional Neural Networks (CNNs) for image segmentation tasks. This research leverages the strengths of CNNs for feature extraction in combination with CRF models for structured prediction, aiming to improve segmentation performance across various benchmarks.

The authors introduce a method that utilizes a pre-trained deep CNN for extracting discriminative features, which are subsequently used to construct potential functions within a CRF framework. This approach is motivated by the limitations of traditional hand-crafted features commonly used in CRF models, such as histograms of oriented gradients (HOG) or scale-invariant feature transform (SIFT) descriptors. The deep CNN employed in this paper is pre-trained on the ImageNet dataset, ensuring a robust feature extraction across diverse visual conditions.

A structured support vector machine (SSVM) is employed for parameter learning in the CRF. This large-margin learning framework facilitates the optimization of CRF parameters, enhancing the model's ability to adapt to complex segmentations scenarios. Notably, the paper also incorporates spatially related co-occurrence pairwise potentials during inference, effectively capturing contextual relationships between objects in an image. This method favors labelings of frequently co-occurring object pairs while penalizing implausible combinations, further enhancing segmentation accuracy.

The segmentation framework is evaluated on several datasets, including Weizmann horse, Graz-02, MSRC-21, Stanford Background, and PASCAL VOC 2011. The reported results indicate that the proposed method outperforms traditional hand-crafted feature approaches, establishing new baseline performances on these datasets. It consistently shows superior performance both in binary segmentation tasks, such as those in the Weizmann horse and Graz-02 datasets, and multi-class segmentation challenges, like MSRC-21.

A critical contribution of the paper is the implementation of the large-margin SSVM within the CRF, demonstrating significant empirical gains. The research also highlights the efficacy of CNN features in the image segmentation domain, encouraging further exploration of transferring deep learning models trained on one task (e.g., image classification) to another (e.g., semantic segmentation).

The implications of this research are substantial, both in practical applications of image segmentation in fields such as autonomous driving, medical imaging, and surveillance, and in theoretical advancements by bridging deep learning architectures with probabilistic graphical models. Future work could explore further integration of advanced deep learning techniques, such as fully convolutional networks (FCNs), within the CRF framework to enhance scalability and accuracy across even larger and more varied datasets.

In conclusion, this paper provides a significant step forward in applying deep learning techniques to traditional CRF-based segmentation tasks, highlighting the potential for hybrid models to surpass the capabilities of stand-alone architectures in complex image analysis tasks.