High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision (1504.06201v3)

Abstract: Most of the current boundary detection systems rely exclusively on low-level features, such as color and texture. However, perception studies suggest that humans employ object-level reasoning when judging if a particular pixel is a boundary. Inspired by this observation, in this work we show how to predict boundaries by exploiting object-level features from a pretrained object-classification network. Our method can be viewed as a "High-for-Low" approach where high-level object features inform the low-level boundary detection process. Our model achieves state-of-the-art performance on an established boundary detection benchmark and it is efficient to run. Additionally, we show that due to the semantic nature of our boundaries we can use them to aid a number of high-level vision tasks. We demonstrate that using our boundaries we improve the performance of state-of-the-art methods on the problems of semantic boundary labeling, semantic segmentation and object proposal generation. We can view this process as a "Low-for-High" scheme, where low-level boundaries aid high-level vision tasks. Thus, our contributions include a boundary detection system that is accurate, efficient, generalizes well to multiple datasets, and is also shown to improve existing state-of-the-art high-level vision methods on three distinct tasks.

• The paper proposes a novel "High-for-Low" method for boundary detection using high-level object features from pretrained networks, shifting from traditional low-level features.
• The method achieves state-of-the-art boundary detection performance on benchmarks like BSDS500, outperforming previous methods and providing near-real-time processing speed.
• The detected boundaries enhance high-level vision tasks like semantic segmentation and object proposal generation ("Low-for-High"), showing significant performance improvements in these applications.

High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision

The paper presents a novel approach to boundary detection in computer vision, based on the use of high-level object features rather than the traditional reliance on low-level features like color and texture. This conceptual shift is grounded in psychological studies that suggest human boundary detection involves object-level reasoning. The proposed method integrates object features extracted from a pretrained object-classification network to enhance boundary detection. Referred to as a "High-for-Low" approach, this method informs the low-level boundary detection process using high-level object cues.

Implemented using a deep neural network architecture, the method utilizes the VGG network, trained for object classification to extract object-level features from images. These high-level features are interpolated and subsequently processed through fully connected layers to produce boundary predictions. Notably, the technique achieves state-of-the-art results on the BSDS500 boundary detection benchmark, demonstrating significant improvement over existing methods.

The research further explores the enhancement of high-level vision tasks using the generated boundaries. By integrating the predicted boundaries, improvements are observed in semantic boundary labeling, semantic segmentation, and object proposal generation. This dual usage, defined as the "Low-for-High" approach, employs low-level boundary cues to inform high-level vision tasks. The results indicate substantial performance gains in each of these tasks over existing state-of-the-art methods.

Empirically, the method consistently surpasses previous approaches on various performance metrics. For instance, boundary detection accuracy improves notably, with the Optimal Dataset Scale (ODS) rising to 0.77 compared to the previous best 0.76, alongside matched advancements in other benchmarks, including 0.68 for per-image Optimal Image Scale (OIS). Additionally, the system demonstrates efficiency, achieving near-real-time processing speeds, significantly faster than prior deep learning-based solutions.

The analysis of the proposed approach reveals crucial insights into the integration of boundary detection with semantic recognition. By leveraging the semantic nature of detected boundaries, there is potential for advanced applications in object recognition systems, particularly where object context and spatial coherence are critical.

The theoretical and practical implications of this research are notable. Theoretically, it confirms the advantage of integrating high-level features for boundary detection, paving the way for future exploration into hybrid systems that combine deep learning with traditional image processing techniques. Practically, the application of these boundaries to enhance other vision tasks shows the versatility and potential widespread utility of the method in varied computer vision problems.

Future research may examine the extension of this approach to more complex datasets and tasks, assessing scalability and adaptiveness in real-world applications. Additionally, exploring the fusion of this method with other state-of-the-art models could further optimize performance, highlighting the potential synergistic improvements possible when integrating advances in deep learning with established vision datasets.

In summary, this paper presents a robust and efficient framework for boundary detection using high-level object features, significantly advancing both boundary detection performance and its application in high-level vision tasks. This dual impact underscores the relevance and potential of such an approach in the broader context of computer vision research and applied solutions.