SEEDS: Superpixels Extracted via Energy-Driven Sampling (1309.3848v1)

Abstract: Superpixel algorithms aim to over-segment the image by grouping pixels that belong to the same object. Many state-of-the-art superpixel algorithms rely on minimizing objective functions to enforce color ho- mogeneity. The optimization is accomplished by sophis- ticated methods that progressively build the superpix- els, typically by adding cuts or growing superpixels. As a result, they are computationally too expensive for real-time applications. We introduce a new approach based on a simple hill-climbing optimization. Starting from an initial superpixel partitioning, it continuously refines the superpixels by modifying the boundaries. We define a robust and fast to evaluate energy function, based on enforcing color similarity between the bound- aries and the superpixel color histogram. In a series of experiments, we show that we achieve an excellent com- promise between accuracy and efficiency. We are able to achieve a performance comparable to the state-of- the-art, but in real-time on a single Intel i7 CPU at 2.8GHz.

• The paper introduces SEEDS, which replaces complex optimization with a simple hill-climbing strategy to efficiently extract superpixels.
• It refines a grid-based initialization using an energy function based on color histogram similarity to ensure high homogeneity and precise boundaries.
• SEEDS achieves competitive performance in real time, processing at 30Hz and meeting state-of-the-art benchmarks in segmentation accuracy.

Overview of SEEDS: Superpixels Extracted via Energy-Driven Sampling

This paper introduces SEEDS, a novel approach for superpixel extraction that balances accuracy with real-time performance. Traditional superpixel algorithms focus on minimizing objective functions to ensure color homogeneity, employing complex optimization methods to construct superpixels through adding cuts or fostering growth, which results in prohibitive computational costs, especially for real-time applications. SEEDS innovates by utilizing a simple hill-climbing optimization strategy to iteratively refine superpixels from an initial partitioning. The authors propose an energy function based on color histogram similarity, allowing SEEDS to attain state-of-the-art performance efficiently.

Methodology

SEEDS begins with a regular grid-based initialization of superpixels, which is subsequently refined through exchanging pixels between neighboring superpixels. The refinement process uses an energy function that consists of two main components:

1. Color Distribution Term: This term enforces color similarity by enhancing homogeneity within superpixels, formulated as:

$H(s) = \sum_k \Psi(c_{\mathcal{A}_k})$

where $\Psi(c_{\mathcal{A}_k})$ measures the concentration of the color histogram within each superpixel.

1. Boundary Term: This optional component encourages smooth boundary shapes, though in practice SEEDS achieves effective boundary updating hierarchically from coarse to fine granularity.

SEEDS employs hierarchical block sizes in its update strategy, beginning with larger blocks and refining them down to the pixel level, allowing the algorithm to operate significantly faster than existing methods.

Results and Performance

The authors evaluate SEEDS against the Berkeley Segmentation Dataset benchmarks, using metrics such as Undersegmentation Error (UE), Boundary Recall (BR), and Achievable Segmentation Accuracy (ASA). SEEDS demonstrates robust performance, matching or exceeding the quality of state-of-the-art methods while achieving real-time processing speeds of 30Hz on a single Intel i7 CPU. The paper highlights that SEEDS provides superpixels competitive with the best non-real-time methods and exhibits superior boundary recall.

Implications and Future Work

The practical implications of SEEDS are considerable, especially for applications requiring real-time processing such as video analysis and autonomous systems. The efficiency and effectiveness of SEEDS stem from its hill-climbing optimization and energy function design, both of which can be generalized or modified for various applications.

On a theoretical level, SEEDS presents a framework that bypasses traditional computational bottlenecks in superpixel extraction. As a future direction, enhancements could involve integrating additional features such as texture or optical flow within the energy function, which may further refine its performance in diverse contexts.

SEEDS represents a significant contribution to the development of superpixel algorithms by demonstrating that sophisticated optimization processes can be supplanted by more streamlined methods without sacrificing performance. As such, SEEDS forms a robust baseline for future innovations in real-time image segmentation.