Visual Saliency Estimation Using Superpixel Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing saliency estimation methods in image processing face challenges in producing pixel-accurate saliency maps that consistently separate foreground and background elements, often resulting in blurry maps that overemphasize local features or lose important details due to high-frequency content and noise.
Innovation Solution
A visual saliency estimation process that decomposes images into compact, perceptually homogeneous elements, computes uniqueness and spatial distribution measures using separable Gaussian filters, and assigns saliency values to produce pixel-accurate maps that uniformly cover objects of interest, while controlling locality and preserving edge information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If Difference of Gaussians approach is used to evaluate low-level features, then computational simplicity is achieved, but the resulting saliency maps become blurry and overemphasize small local features
Solution Approach 1:
The patent segments the image into superpixels (compact, perceptually homogeneous regions) before computing saliency measures. This segmentation allows the method to evaluate contrast at a region level rather than pixel level, reducing the emphasis on small local features while maintaining computational efficiency. The superpixel segmentation serves as a preprocessing step that groups pixels into meaningful regions, thereby resolving the contradiction between simplicity and precision.
2Stability of the object's composition
If global methods consider contrast relationships over the complete image, then global image structures are preserved, but combinatorial complexity increases and small salient details are lost
Solution Approach 1:
The patent divides the image into a reduced set of superpixels, which dramatically reduces the combinatorial complexity compared to evaluating all pixel pairs globally. This segmentation maintains global structure by considering relationships between regions rather than individual pixels, while preserving small salient details within each superpixel. The approach achieves global consistency without the computational burden of full combinatorial analysis.
3Measurement precision
If local methods estimate saliency based on immediate image neighborhoods, then less blurry saliency maps are produced, but global relations and structures are ignored and sensitivity to high frequency content increases
Solution Approach 1:
The patent extends the analysis from local pixel neighborhoods to a multi-scale framework by computing saliency measures at different spatial scales and combining them. This dimensional extension allows the method to capture both local sharpness (through fine-scale analysis) and global relations (through coarse-scale analysis). The multi-scale approach resolves the contradiction by operating simultaneously at multiple levels of abstraction.
4Stability of the object's composition
If Gaussian blur is applied to decrease noise influence, then globally more consistent results are achieved, but spatial relationships inside the image are not accounted for and background regions may be highlighted as salient
Solution Approach 1:
The patent applies different processing treatments to different regions of the image based on their local characteristics. Superpixels that represent foreground objects receive different saliency weighting compared to background regions. This local quality approach allows Gaussian blur to be applied selectively to reduce noise in appropriate regions while preserving spatial relationships and preventing background regions from being incorrectly highlighted as salient.
Data Source
AI summary
Methods and apparatus for deriving a saliency measure for images and video are described. In an embodiment, a process includes decomposing, by a processor, an image into elements, wherein elements cluster the image and each element consists of spatially connected pixels. The processor then calculates a first image measure indicative of each element's uniqueness in the image on a per element basis, and calculates a second image measure indicative of each element's spatial distribution in the image on a per element basis. The processor then provides a per element saliency measure by combining the first image measure and the second image measure.