Structure-measure: A New Way to Evaluate Foreground Maps (1708.00786v1)

Abstract: Foreground map evaluation is crucial for gauging the progress of object segmentation algorithms, in particular in the filed of salient object detection where the purpose is to accurately detect and segment the most salient object in a scene. Several widely-used measures such as Area Under the Curve (AUC), Average Precision (AP) and the recently proposed Fbw have been utilized to evaluate the similarity between a non-binary saliency map (SM) and a ground-truth (GT) map. These measures are based on pixel-wise errors and often ignore the structural similarities. Behavioral vision studies, however, have shown that the human visual system is highly sensitive to structures in scenes. Here, we propose a novel, efficient, and easy to calculate measure known an structural similarity measure (Structure-measure) to evaluate non-binary foreground maps. Our new measure simultaneously evaluates region-aware and object-aware structural similarity between a SM and a GT map. We demonstrate superiority of our measure over existing ones using 5 meta-measures on 5 benchmark datasets.

• The paper presents Structure-measure, a novel metric integrating region‐aware and object‐aware evaluations to overcome the limitations of traditional pixel-wise error metrics.
• It employs comprehensive experiments on five benchmarks and a human study, demonstrating superior alignment with human visual perception compared to AUC, AP, and Fbw.
• The measure significantly advances salient object detection by improving ranking consistency and holds potential for broader applications in computer vision tasks.

Structure-measure: A New Way to Evaluate Foreground Maps

Foreground map evaluation is pivotal in assessing the efficacy of object segmentation algorithms, specifically in the salient object detection domain. The paper "Structure-measure: A New Way to Evaluate Foreground Maps" by Deng-Ping Fan et al. proposes a novel evaluation measure that addresses the deficiencies inherent in traditional pixel-wise error-based metrics.

Overview

The primary objective of salient object detection is to accurately identify and segment the most prominent object within a scene. Traditional evaluation metrics like Area Under the Curve (AUC), Average Precision (AP), and $F_{\beta}^{\omega}$ (Fbw) focus on pixel-wise errors and often neglect structural similarities crucial to human visual perception. The proposed measure, termed "Structure-measure," aims to evaluate both region-aware and object-aware structural similarity between a saliency map (SM) and a ground-truth (GT) map.

Contributions and Implications

This paper introduces several significant contributions:

1. Region-aware Structural Similarity: This component captures the structural information of object parts by dividing the image into multiple blocks and assessing their individual similarities. The measure incorporates weights proportional to the GT foreground region to enhance its accuracy.
2. Object-aware Structural Similarity: This component evaluates the global distribution and contrast between foreground and background regions in the SM and GT maps. It leverages properties such as sharp foreground-background contrast and uniform distribution to ensure the measure captures the salient object's overall structure accurately.
3. Empirical Evaluation: The superiority of the Structure-measure is demonstrated through comprehensive experiments on five benchmark datasets. The measure consistently outperformed traditional metrics using five meta-measures, with a new meta-measure introduced by the authors to further validate their approach.
4. Human Judgments: A behavioral paper with 45 subjects was conducted, showing that the saliency maps selected by the Structure-measure were more aligned with human judgments when compared to those chosen by AP, AUC, and Fbw.

Numerical Results and Bold Claims

Several key numerical results substantiate the efficacy of the Structure-measure:

• In one experiment, the SalCut algorithm was employed, and the Structure-measure produced the best ranking consistency among alternative methods, as evaluated by the 1-Spearman’s $\rho$ measure.
• In the "State-of-the-art vs. Generic" meta-measure, it showed drastic reduction in errors, demonstrating higher sensitivity to quality outputs from advanced models versus generic maps.
• The new measure exhibited a considerable reduction in errors related to ground-truth switches in the "Ground-truth Switch" meta-measure, being ten times more effective compared to the next best measure.

Theoretical and Practical Implications

Theoretically, this work highlights the limitations of traditional pixel-wise error metrics and emphasizes the importance of structural similarity in visual perception. By integrating both region-aware and object-aware evaluations, the proposed measure provides a more holistic and perceptually relevant assessment.

Practically, the Structure-measure can influence the development and evaluation of future salient object detection models. By providing a more reliable evaluation cue, researchers can fine-tune their models to produce outputs that are not only pixel-accurate but also structurally coherent, aligning better with human visual assessment.

Future Potential

The Structure-measure opens up several avenues for future development:

• Broader Application: While the paper focuses on salient object detection, the underlying principles could be extended to other computer vision tasks, such as instance segmentation and object recognition.
• Optimization and Efficiency: Further research could optimize the computational aspects of the measure, making it suitable for real-time applications in resource-constrained environments like mobile devices.
• Integration with Learning Frameworks: The measure could be integrated as a loss function in deep learning frameworks, driving the models to learn structural features more effectively during training.

In conclusion, the Structure-measure offers a robust and perceptually aligned evaluation framework that addresses the deficiencies of conventional pixel-wise metrics. Its dual focus on regional and global structural similarities makes it a significant advancement in the objective evaluation of salient object detection models and potentially other computer vision tasks.