Salient Object Detection: A Benchmark (1501.02741v2)

Abstract: We extensively compare, qualitatively and quantitatively, 40 state-of-the-art models (28 salient object detection, 10 fixation prediction, 1 objectness, and 1 baseline) over 6 challenging datasets for the purpose of benchmarking salient object detection and segmentation methods. From the results obtained so far, our evaluation shows a consistent rapid progress over the last few years in terms of both accuracy and running time. The top contenders in this benchmark significantly outperform the models identified as the best in the previous benchmark conducted just two years ago. We find that the models designed specifically for salient object detection generally work better than models in closely related areas, which in turn provides a precise definition and suggests an appropriate treatment of this problem that distinguishes it from other problems. In particular, we analyze the influences of center bias and scene complexity in model performance, which, along with the hard cases for state-of-the-art models, provide useful hints towards constructing more challenging large scale datasets and better saliency models. Finally, we propose probable solutions for tackling several open problems such as evaluation scores and dataset bias, which also suggest future research directions in the rapidly-growing field of salient object detection.

Salient Object Detection: A Benchmark

Overview

The paper "Salient Object Detection: A Benchmark" presents an extensive evaluation of state-of-the-art models for salient object detection and segmentation. Comprising 41 models—including those for salient object detection, fixation prediction, objectness measurement, and a baseline—the paper benchmarks their performance across seven challenging datasets: MSRA10K, THUR15K, ECSSD, JuddDB, DUT-OMRON, SED2, and PASCAL-S. This comprehensive assessment reveals significant advances in accuracy and runtime efficiency over the past few years and highlights the predominant strategies and challenges in the field.

Models and Evaluation Metrics

Compared Models

The paper compares 29 salient object detection models, 10 fixation prediction models, and an object proposal model, along with a baseline. The saliency models include techniques such as:

• Adaptive center-surround methods.
• Frequency-tuned visual saliency.
• Graph-based manifold ranking.
• Deep learning-based approaches.

Models designed specifically for salient object detection generally outperform those aimed at related tasks like fixation prediction and object proposal generation, which underscores the importance of task-specific design.

Evaluation Metrics

Four primary metrics were employed to evaluate the models:

1. Precision-Recall (PR) Curves: Used to analyze the overlap between model-generated masks and ground-truth annotations.
2. Receiver Operating Characteristics (ROC) Curves: Evaluate the true positive rate against the false positive rate.
3. Mean Absolute Error (MAE): Measures the average per-pixel error between the predicted and ground-truth saliency maps.
4. F-measure and F-beta weighted measure: Harmonic mean metrics combining precision and recall to offer a balanced overview of model performance.

The paper also explores advanced segmentation techniques, analyzing methods such as adaptive thresholding and the SaliencyCut algorithm.

Findings

Performance Analysis

• Top Performers: DRFI, DSR, and MC models consistently rank among the best across the datasets. DRFI, in particular, demonstrates superior performance due to its discriminative feature integration.
• Runtime Considerations: The paper emphasizes the balance between efficacy and efficiency, noting that while some models like DRFI perform well, they do so at the cost of increased computational time.
• Center Bias: The paper acknowledges the impact of center bias and evaluates model performance on datasets with varying degrees of center bias. Models not relying heavily on center bias, such as DRFI, retain strong performance even on off-center cases.
• Salient Object Existence: Evaluations on background-only images highlight the need for models to adapt to cases where no salient object exists, which remains an area needing further attention.

Dataset and Metric Insights

• Dataset Complexity: JuddDB, PASCAL-S, and THUR15K are identified as more challenging due to less pronounced center bias and higher background clutter.
• Segmentation Techniques: The SaliencyCut algorithm combined with top-performing models yields higher segmentation accuracy, particularly in datasets adhering to single-object scenarios. Multi-object scenes present additional challenges.
• Evaluation Metrics: The paper points out that while PR curves provide more detailed insights than ROC curves, all metrics need to be considered for a comprehensive performance assessment.

Implications and Future Directions

The findings suggest several key directions for future research:

• Integration of High-level Priors: Current models rely heavily on low-level features. Incorporating high-level semantic information may enhance performance, especially in complex and cluttered scenes.
• Handling Complex Scenes and Backgrounds: Improving robustness in scenes with multiple objects and cluttered backgrounds is essential. This includes better detecting small objects and differentiating them from complex backgrounds.
• Leveraging Deep Learning: The promising performance of CNN-based methods highlights the potential of deep learning for salient object detection. Future works could explore more sophisticated architectures and training techniques tailored for saliency tasks.
• Application in Diverse Fields: Expanding the application of saliency detection to areas like human-robot interaction, scene understanding, and cross-modal tasks (e.g., language and vision) represents an exciting frontier.

Conclusion

The paper provides a rigorous and detailed benchmark that reflects the rapid advancements and remaining challenges in salient object detection. By systematically comparing various models and identifying their strengths and weaknesses, it sets the stage for further innovations and applications in computer vision and beyond. The paper underscores the importance of task-specific designs and the necessity of addressing biases and practical constraints to develop more versatile and accurate models.