Salient Object Segmentation via Multi-Linear Exemplar Regressors
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Solution Overview
Problem
Current image salient object segmentation models face challenges in distinguishing salient objects from non-salient objects, especially in complex scenarios where they share similar visual attributes, and require large datasets for training, with deep learning methods being opaque about contributing visual attributes.
Innovation Solution
A method for salient object segmentation using multi-linear exemplar regressors, which involves constructing a benchmark dataset to analyze intrinsic visual attributes, employing manifold-preserving foreground propagation, and aggregating linear exemplar regressors to generate a foreground probability map and suppress non-salient objects, adapting to various complexity scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used for salient object segmentation, then segmentation capability in complex scenarios is improved, but model interpretability deteriorates due to black box nature
Solution Approach 1:
The patent segments the complex deep learning model into multiple independent linear exemplar regressors, each responsible for detecting specific visual attributes (color, texture, shape). This segmentation allows the model to maintain high segmentation accuracy while providing interpretable information about which visual attributes contribute to salient object detection, resolving the black box problem.
Solution Approach 2:
The patent transforms the deep learning model parameters into a set of linear exemplar regressors with explicit visual attribute parameters. By changing the parameter representation from hidden neural network weights to interpretable visual attribute weights, the model maintains accuracy while enabling analysis of which visual attributes (color, texture, shape) most contribute to detection decisions.
2Measurement precision
If comprehensive visual attributes are used to characterize objects, then segmentation capability is improved, but computational complexity increases
Solution Approach 1:
The patent extracts only the most relevant visual attributes (color, texture, shape) that are necessary for salient object detection, rather than using all possible comprehensive attributes. This extraction approach maintains segmentation capability while reducing computational complexity by focusing on the essential visual features.
Solution Approach 2:
The patent uses a subset of visual attributes that are sufficient for effective segmentation rather than exhaustively analyzing all possible attributes. By applying partial action (using only necessary attributes), the model achieves good segmentation performance with reduced computational burden compared to exhaustive attribute analysis.
3Measurement precision
If multiple linear exemplar regressors are aggregated, then segmentation accuracy in complex scenarios is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary action by pre-training multiple linear exemplar regressors on different visual attributes during the training phase. During inference, these pre-trained regressors can be quickly aggregated without requiring extensive computation, thus improving segmentation accuracy while minimizing additional processing time compared to training a single complex model.
Data Source
AI summary
Provided is a method for salient object segmentation of an image by aggregating a multi-linear exemplar regressors, including: analyzing and summarizing visual attributes and features of a salient object and a non-salient object using background prior and constructing a quadratic optimization problem, calculating an initial saliency probability map, selecting a most trusted foreground and a background seed point, performing manifold preserving foreground propagation, generating a final foreground probability map; generating a candidate object set for the image via an objectness adopting proposal, using a shape feature, a foregroundness and an attention feature to characterize each candidate object, training the linear exemplar regressors for each training image to characterize a particular saliency pattern of the image; aggregating a plurality of linear exemplar regressors, calculating saliency values for the candidate object set of a test image, and forming an image salient object segmentation model capable of processing various complex scenarios.