Salient Object Segmentation via Multi-Scale Saliency Maps
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Solution Overview
Problem
Current computational models for identifying salient objects in images often rely on simplistic methods that fail to accurately distinguish objects from their context, leading to inefficiencies in image searches and analyses.
Innovation Solution
A process that segments an input image into regions, calculates saliency values based on scale levels, constructs saliency maps, and combines them to define a shape prior, then minimizes an energy function to refine the appearance and shape of the salient object, enabling more accurate identification and segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simplistic computational models are used to identify salient objects, then the device complexity is reduced, but the measurement precision of salient object identification deteriorates
Solution Approach 1:
The patent segments the image processing task into multiple components: region segmentation divides the image into multiple regions, scale-level segmentation processes different size ranges separately, and the energy function segments the salient object from background through pixel labeling. This multi-level segmentation approach improves identification accuracy without requiring a single overly complex model.
Solution Approach 2:
The patent introduces multiple dimensions to the analysis: scale levels (processing images at different resolutions), spatial dimensions (x, y coordinates), and energy dimensions (data term, smoothness term, shape prior). By analyzing salient objects across multiple dimensions rather than a single view, the system achieves higher precision without exponentially increasing model complexity.
2Measurement precision
If multi-scale segmentation and energy minimization are applied, then the measurement precision of salient object identification is improved, but the productivity of image processing deteriorates
Solution Approach 1:
The patent divides the computationally intensive task into manageable segments: first segmenting at coarse scale levels to identify candidate regions, then applying detailed energy minimization only to those candidates rather than the entire image. This segmented approach maintains high precision while improving processing speed by avoiding exhaustive analysis of all pixels at all scales.
Solution Approach 2:
The patent applies the full multi-scale energy minimization process selectively rather than universally. It performs comprehensive analysis on regions likely to contain salient objects while using simplified or skipped processing for background regions. This partial application of the complex algorithm maintains accuracy for critical regions while improving overall productivity.
3Measurement precision
If context-based saliency calculation is performed, then the measurement precision of object distinction is improved, but the use of energy and computational resources worsens
Solution Approach 1:
The patent segments the computational domain into multiple scale levels and spatial regions. Context-based saliency calculations are performed at coarser scales first to identify promising regions, then refined at finer scales only where needed. This segmented energy expenditure achieves high distinction precision while managing computational resource consumption through hierarchical processing.
Solution Approach 2:
The patent performs preliminary saliency calculations at coarse scale levels before committing to detailed analysis. This preliminary action identifies candidate regions that are likely to contain salient objects, allowing the system to focus computational energy on promising areas rather than uniformly processing the entire image at high resolution, thus improving efficiency.
Data Source
AI summary
Techniques for identifying a salient object with respect to its context are described. A process receives an input image that includes a salient object. The process segments the input image into multiple regions and calculates a saliency value for each of the segmented regions based on scale image levels. The process constructs saliency maps based at least in part on the calculated saliency value, and combines the saliency maps to construct a total saliency map. Next, the process connects a set of line segments computed from the input image and utilizes the total saliency map to compute a closed boundary, which forms a shape prior from the closed boundary, and extracts the salient object from the total saliency map and the shape prior.