Direction-aware Spatial Context Features for Shadow Detection (1712.04142v2)

Abstract: Shadow detection is a fundamental and challenging task, since it requires an understanding of global image semantics and there are various backgrounds around shadows. This paper presents a novel network for shadow detection by analyzing image context in a direction-aware manner. To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN. By learning these weights through training, we can recover direction-aware spatial context (DSC) for detecting shadows. This design is developed into the DSC module and embedded in a CNN to learn DSC features at different levels. Moreover, a weighted cross entropy loss is designed to make the training more effective. We employ two common shadow detection benchmark datasets and perform various experiments to evaluate our network. Experimental results show that our network outperforms state-of-the-art methods and achieves 97% accuracy and 38% reduction on balance error rate.

• The paper introduces a deep learning framework utilizing direction-aware spatial context features extracted via a spatial RNN within a CNN for improved shadow detection in single images.
• Evaluated on benchmark datasets, the network achieved a 97% accuracy and a 38% reduction in balance error rate over existing state-of-the-art methods.
• This direction-aware approach has potential implications for other computer vision tasks requiring context-aware interpretation, such as saliency detection and semantic segmentation.

Direction-aware Spatial Context Features for Shadow Detection

The paper presents a novel deep learning framework designed to address the challenge of shadow detection in single images. The research focuses on enhancing shadow detection by leveraging direction-aware spatial context features, which are extracted using a customized spatial recurrent neural network (RNN) architecture embedded within a convolutional neural network (CNN).

Key Contributions

1. Direction-aware Attention Mechanism: The paper introduces a direction-aware attention mechanism implemented within a spatial RNN. This mechanism plays a pivotal role in understanding image semantics by assigning attention weights to spatial context features, enabling improved shadow detection performance. These weights are learned through training, allowing the network to recover direction-aware spatial context (DSC) tailor-made for identifying shadows.
2. DSC Module: The authors design the DSC module to learn spatial contexts with directional awareness. The module aggregates spatial contexts along four principal directions—left, right, up, and down—utilizing the formulated attention mechanism. This results in effective DSC feature extraction at different levels within the CNN.
3. Novel Loss Function: To balance the shadow detection network's sensitivity to both shadow and non-shadow regions, a weighted cross-entropy loss is crafted. This loss function is particularly critical due to the inherent imbalance of shadow versus non-shadow pixels in typical images.
4. Benchmark Evaluation: Utilizing the SBU Shadow Dataset and UCF Shadow Dataset, the paper evaluates the proposed network. The results are promising, demonstrating superiority over existing state-of-the-art shadow detection methods with an impressive 97% accuracy and a notable 38% reduction in balance error rate.

Implications and Future Work

The proposed framework has implications beyond shadow detection, with potential applications in areas such as saliency detection and semantic segmentation. By considering directional context, the network effectively discerns subtle shadows and reduces false positives associated with dark objects misclassified as shadows. The integration of learned attention weights to modulate spatial context is a technique that could find utility in various computer vision tasks requiring context-aware interpretation.

In terms of future developments, the paper hints at extending the framework to video-based shadow detection, wherein temporal variations in shadows could be examined. Additionally, adapting the direction-aware spatial context approach to other domains might further reveal its generalizability and robustness in complex visual recognition tasks.

Overall, the paper provides a structured methodology for enhancing shadow detection leveraging deep learning in conjunction with attention mechanisms focused on directional context. This approach exemplifies how spatial reasoning and innovative neural network design can intersect to tackle traditionally challenging computer vision problems.