Semantic Filter Network for Image Co-segmentation
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Solution Overview
Problem
Current co-segmentation techniques face challenges in accurately labeling objects of a common class across multiple images while maintaining computational efficiency, often failing to segment objects that are partially occluded or distant, and requiring significant computational resources.
Innovation Solution
The use of a semantic guided filter network that propagates high-level convolutional neural network feature activations between images, employing super-pixels to reduce computational load and using dilated convolutions to predict foreground/background masks, allowing for efficient co-segmentation of objects even if not all images contain the common object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current co-segmentation techniques are used to accurately label objects of a common class across multiple images, then segmentation precision is improved, but computational resources required increase significantly
Solution Approach 1:
The patent segments the image processing task by first dividing images into super-pixels (coarse segmentation), then processing at the super-pixel level, and finally refining to pixel-level segmentation. This hierarchical segmentation approach reduces the computational burden by operating on fewer, larger units initially, while still achieving precise pixel-level results in the final stage.
Solution Approach 2:
The patent introduces super-pixels as an intermediary representation between raw pixels and final segmentation masks. By computing super-pixel features and using them as intermediate processing units, the system reduces computational complexity while maintaining segmentation accuracy, as super-pixels serve as a bridge that simplifies the data structure before final classification.
2Measurement precision
If current co-segmentation techniques process all pixels to ensure accurate segmentation, then segmentation precision is improved, but processing speed decreases
Solution Approach 1:
The patent applies segmentation by dividing images into super-pixels and processing them as unified units. This allows the system to perform feature extraction and classification on super-pixel representations rather than individual pixels, significantly reducing the number of processing units while maintaining boundary accuracy through subsequent refinement steps.
Solution Approach 2:
The patent employs partial action by first performing co-segmentation at the super-pixel level to identify candidate regions, then applying refined classification only to boundary regions and ambiguous areas. This selective processing approach maintains precision where needed while reducing overall computational effort compared to processing every pixel uniformly.
3Reliability
If current techniques attempt to segment all objects in images, then completeness of segmentation is improved, but computational complexity increases
Solution Approach 1:
The patent extracts and processes only the relevant features and regions necessary for co-segmentation. By using dilated convolutions to focus on specific spatial patterns and by processing super-pixel representations rather than full images, the system extracts essential information while discarding redundant data, reducing computational complexity while maintaining segmentation completeness.
Solution Approach 2:
The patent segments the processing task into distinct stages: super-pixel formation, super-pixel feature extraction, co-segmentation at super-pixel level, and final pixel-level refinement. This multi-stage segmentation of the processing pipeline allows each stage to focus on specific aspects, reducing overall computational complexity while ensuring complete coverage of all objects.
4Measurement precision
If current techniques process multiple images simultaneously to identify common objects, then co-segmentation accuracy is improved, but processing time increases
Solution Approach 1:
The patent merges multiple images by computing super-pixel representations and aggregating features across images at the super-pixel level. By combining information from multiple images before final classification, the system achieves improved co-segmentation accuracy through fused features while reducing processing time compared to handling each image separately through the entire pipeline.
Data Source
AI summary
Devices and techniques are generally described for object co-segmentation in image data using a semantic filter. In various examples, first image data and second image data may be received. First semantic feature data may be determined for the first image data and second semantic feature data may be determined for the second image data. Filtered semantic feature data corresponding to the first semantic feature data may be generated by filtering the first semantic feature data and the second semantic feature data in a semantic feature space. A determination may be made that a first pixel corresponds to a first class of an object, where at least one object of the first class is represented in the first image data and the second image data.