Deep Saliency with Encoded Low level Distance Map and High Level Features (1604.05495v1)

Abstract: Recent advances in saliency detection have utilized deep learning to obtain high level features to detect salient regions in a scene. These advances have demonstrated superior results over previous works that utilize hand-crafted low level features for saliency detection. In this paper, we demonstrate that hand-crafted features can provide complementary information to enhance performance of saliency detection that utilizes only high level features. Our method utilizes both high level and low level features for saliency detection under a unified deep learning framework. The high level features are extracted using the VGG-net, and the low level features are compared with other parts of an image to form a low level distance map. The low level distance map is then encoded using a convolutional neural network(CNN) with multiple 1X1 convolutional and ReLU layers. We concatenate the encoded low level distance map and the high level features, and connect them to a fully connected neural network classifier to evaluate the saliency of a query region. Our experiments show that our method can further improve the performance of state-of-the-art deep learning-based saliency detection methods.

• The paper introduces a model that combines VGG16 high-level features with an encoded low-level distance map to improve saliency detection precision.
• It employs 1×1 convolutional layers to capture subtle local contrasts, resulting in sharper boundary preservation and precise object localization.
• Empirical results demonstrate superior performance against state-of-the-art methods across multiple benchmarks, indicating potential for real-time computer vision applications.

Insightful Overview of "Deep Saliency with Encoded Low level Distance Map and High Level Features"

The paper "Deep Saliency with Encoded Low level Distance Map and High Level Features" presents a novel approach to saliency detection in images, integrating deep learning and hand-crafted feature methodologies to improve performance. This work is particularly relevant in the domain of computer vision, where accurately identifying salient regions in images holds significant implications across various applications such as image cropping, object detection, and video summarization.

Methodology

The authors propose a unified framework combining high level features extracted from the VGG16 model—a proven deep convolutional neural network (CNN) for image recognition—and encoded low level distance maps. Unlike prior approaches relying solely on either deep learning or hand-crafted features, this approach leverages both, suggesting that low level features can enhance the precision of saliency maps by providing complementary characteristics to the coarse spatial features obtained from deep CNNs.

The core innovation is the development of the Encoded Low level Distance map (ELD-map), which encodes feature distances between superpixels using convolutional layers with $1 \times 1$ kernels within a CNN. This encoding aims to capture the discriminative power required to differentiate between subtle local contrasts in an image that traditional high level features blur due to convolutional and pooling layer processing.

Strong Results

Quantitatively, the method exhibits superior performance over existing saliency detection algorithms across multiple benchmark datasets, including ASD, PASCAL-S, ECSSD, DUT-OMRON, and THUR15K. The reported improvements are attributed to calculated fusion of high and low level cues, resulting in sharper boundary preservation and precise salient object localization. The model achieves higher maximum F-measure scores and lower mean absolute error (MAE) compared to state-of-the-art methods like MCDL and MDF, affirming the efficacy of the proposed dual-feature integration.

Implications and Future Directions

The implications of this dual-feature approach are substantial. By efficiently combining cues from both end-to-end learned and manually engineered feature spaces, the methodology extends the applicability of saliency detection models to more complex scenes where single-method models struggle, such as low-contrast images and those with intricate backgrounds. The efficiency of the model, demonstrated by its runtime performance, further reinforces its potential for real-time applications.

Looking forward, the authors suggest exploring more sophisticated CNN architectures or potentially increasing the dataset diversity to enhance model robustness against edge cases like small-scale or boundary-touching salient objects. The integration of more diverse data during training could mitigate observed shortcomings and push the boundaries of saliency detection capabilities.

Conclusion

This paper contributes a significant method for improving saliency detection by bridging the gap between high and low level feature utilizations in a deep learning context. The demonstrated improvements in precision and processing efficiency suggest that similar integrative approaches could be beneficial across a broader range of computer vision tasks, inviting further research into hybrid feature model architectures within AI development.

In conclusion, the incorporation of the ELD-map into saliency detection frameworks signifies a meaningful stride forward, enhancing the capability of machines to mimic human-like attention mechanisms, thus offering more nuanced visual recognition systems.