Derivative Image Identification via Micro-Feature Histograms

The patent segments the image into multiple regions and extracts local features from each region, then combines these features into a comprehensive feature vector for the entire image. This segmentation approach allows the system to efficiently compare images by focusing on distinctive local patterns rather than processing the entire image uniformly, thereby reducing computational complexity while maintaining detection accuracy.

The patent creates a feature representation (copy) of the image that captures its essential characteristics through extracted features and histograms. Instead of directly comparing pixel data of the original images, the system compares these feature representations, which significantly reduces the computational burden while preserving the ability to identify derivative images through various manipulations.

The patent extracts local features from specific regions of the image and uses these localized characteristics to build the overall image representation. By focusing on local patterns and their distributions rather than global properties, the system can more accurately distinguish between genuine derivative images and unrelated images, reducing false positives while maintaining high detection accuracy for actual manipulations.

The patent transforms image data into different parameter spaces by extracting features and creating histograms that represent the distribution of visual characteristics. This parameter transformation allows for more robust comparison that is less sensitive to minor variations and manipulations, thereby reducing false positives while maintaining accurate detection of genuine derivative relationships.