Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation (1503.00848v4)

Abstract: We propose a unified approach for bottom-up hierarchical image segmentation and object proposal generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object proposals by exploring efficiently their combinatorial space. We also present Single-scale Combinatorial Grouping (SCG), a faster version of MCG that produces competitive proposals in under five second per image. We conduct an extensive and comprehensive empirical validation on the BSDS500, SegVOC12, SBD, and COCO datasets, showing that MCG produces state-of-the-art contours, hierarchical regions, and object proposals.

• The paper introduces a multiscale combinatorial grouping method that significantly improves image segmentation and object proposal generation.
• It leverages a fast normalized cuts algorithm achieving a 20× speed-up and employs hierarchical segmentation across multiple resolutions for refined results.
• The approach demonstrates state-of-the-art contour detection and robust object proposals validated on benchmarks like BSDS500, SegVOC12, SBD, and COCO.

Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation

Overview

The paper introduces a unified approach termed Multiscale Combinatorial Grouping (MCG) designed to enhance image segmentation and object proposal generation. MCG leverages multiscale information to improve the accuracy of object proposals, which are crucial for object recognition tasks. The authors also present a faster variant known as Single-scale Combinatorial Grouping (SCG), which achieves competitive performance with reduced computational requirements.

Key Contributions

The paper's major contributions are outlined below:

1. Fast Normalized Cuts Algorithm: The authors develop an efficient algorithm to perform normalized cuts, resulting in a significant reduction in computation time for eigenvector calculations without losing performance. This offers a 20× speed-up, allowing practical applicability in large-scale settings.
2. Hierarchical Segmentation Leveraging Multiscale Information: By constructing hierarchical segmentations across multiple image resolutions, the proposed system enhances segmentation quality. The authors employ an alignment strategy to project coarse segmentations onto finer details, thus maintaining alignment across scales.
3. Combinatorial Grouping Strategy: The approach uses a combinatorial exploration of regions within the hierarchy to generate object proposals. By leveraging the multiscale segmentation results, the method examines potential combinations of regions to identify high-quality object proposals efficiently.

Empirical Validation

The authors substantiate the effectiveness of their approach through extensive experiments on benchmark datasets such as BSDS500, SegVOC12, SBD, and COCO. The results reveal:

• State-of-the-Art Contour and Region Quality: MCG achieves superior performance in contour detection and region segmentation tasks, surpassing existing methods including gPb-UCM.
• High-Quality Object Proposals: The method demonstrates leading accuracy in generating object proposals, crucial for downstream tasks in object recognition.
• Efficiency: MCG attains its results efficiently, delivering a robust set of proposals that are computationally feasible for real-world applications.

Implications and Future Directions

This work significantly impacts the development of object recognition systems by improving the quality and efficiency of object proposals. The robust performance across various datasets suggests that MCG and SCG could be valuable for broad applications, from robotics to autonomous driving.

Future research could focus on integrating these techniques with advanced deep learning models, potentially enhancing state-of-the-art object detection frameworks. There is also room for exploring generalized applications beyond the current datasets, driving advancements in areas demanding fine-grained image analysis. The considerations of scalability and computational limits in MCG present a valuable dialogue for future exploration in efficient algorithm design.

In summary, this paper offers a substantial contribution to computer vision, particularly in proposing effective methodologies for image segmentation and object proposal generation.