Image Segmentation Using Thresholded Multi-Category Classification

The patent segments the training process into two distinct phases: first training a pixel classifier using single-category images for rapid convergence, then performing multi-category classification on semi-occluded regions using the learned features. This segmentation allows the system to benefit from both fast training and accurate segmentation in complex scenes.

The patent implements a dynamic classification approach where the system adaptively switches between single-category and multi-category classification modes based on the scene characteristics. In normal regions, it uses fast single-category classification, while in semi-occluded regions detected through probability thresholds, it employs more thorough multi-category analysis.

The patent segments the image into different regions based on classification confidence and occlusion probability. In regions where the uniqueness assumption holds, simple single-category classification is used. In regions where multiple categories are likely (semi-occluded areas), the system performs multi-category classification to maintain accuracy without unnecessarily increasing complexity throughout the entire image.

The patent applies different classification strategies to different regions of the image. Normal regions use simple single-category classification, while semi-occluded regions use multi-category classification. This local adaptation of classification complexity ensures high accuracy where needed while maintaining overall system efficiency.

The patent implements a dynamic classification strategy that adapts the level of analysis based on scene characteristics. The system first performs rapid single-category classification on all pixels, then identifies semi-occluded regions using probability thresholds and occlusion detection, and finally applies multi-category classification only to these identified regions. This dynamic approach maintains high accuracy while minimizing processing time.

The patent applies the more computationally intensive multi-category classification only partially - specifically to semi-occluded regions where it is most needed - rather than applying it to all pixels. This partial application of the enhanced method achieves the necessary accuracy improvement without the full computational cost of universal multi-category classification.