Panoramic Image Classification via Pixel Boundary Analysis
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Solution Overview
Problem
Current digital image asset manipulation and management applications face challenges in accurately distinguishing between panoramic and non-panoramic images, particularly due to inadequate metadata recognition and storage limitations, which affects image classification and management.
Innovation Solution
A framework that uses a trained neural network and analyzes image characteristics, such as pixel value equality and dynamic range, to classify images as panoramic or non-panoramic, and determines the presence of synthetic content to facilitate accurate classification and enhance search rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If metadata is used to identify panoramic images, then image classification can be achieved, but metadata may be discarded or fail to recognize non-standard formats reducing reliability
Solution Approach 1:
The patent introduces an intermediary classification mechanism that analyzes image characteristics (aspect ratio, pixel value distribution, boundary characteristics) as a mediator between the unreliable metadata and the final classification decision. This intermediary analysis verifies and supplements metadata-based classification, ensuring accurate identification of panoramic images even when metadata is missing or non-standard.
Solution Approach 2:
The system enables images to self-classify by analyzing their own intrinsic characteristics such as aspect ratio, pixel value distribution, and boundary properties. This self-service mechanism allows images to be accurately classified based on their inherent properties without relying solely on external metadata, thereby improving classification reliability.
2Ease of manufacture
If all images are stored in a single location and format, then storage simplicity is maintained, but image management and manipulation become less efficient
Solution Approach 1:
The patent segments the image storage and management system by classifying images into distinct categories (panoramic and non-panoramic) based on their characteristics. This segmentation enables different storage locations, formats, and management procedures for different image types, improving management efficiency while maintaining overall system organization.
Solution Approach 2:
The system changes the organizational parameters of image storage by introducing classification-based categorization. Images are organized according to their detected characteristics (aspect ratio, pixel distribution, boundary properties), transforming the storage structure from a single uniform location to multiple categorized locations, thereby enhancing management productivity.
3Measurement precision
If a neural network trained on natural images is used to classify images, then classification works for natural content, but accuracy decreases for synthetic content
Solution Approach 1:
The patent applies local quality by analyzing specific local characteristics of images such as boundary pixel values, aspect ratio, and pixel value distribution patterns. These localized features serve as additional cues that help the classification system distinguish between natural and synthetic images, complementing the neural network's global analysis and improving versatility.
Solution Approach 2:
The classification system combines multiple approaches into a composite methodology: neural network analysis trained on natural images, rule-based classification for synthetic content detection, and characteristic analysis (aspect ratio, pixel distribution, boundary properties). This composite approach leverages the strengths of each method to achieve high accuracy across both natural and synthetic image types.
Data Source
AI summary
Embodiments herein describe a framework for classifying images. In some embodiments, it is determined whether an image includes synthetic image content. If it does, characteristics of the image are analyzed to determine if the image includes characteristics particular to panoramic images (e.g., possess a threshold equivalency of pixel values among the top and/or bottom boundaries of the image, or a difference between summed pixel values of the pixels comprising the right vertical boundary of the image and summed pixel values of the pixels comprising the left vertical boundary of the image being less than or equal to a threshold value). If the image includes characteristics particular to panoramic images, the image is classified as a synthetic panoramic image. If the image is determined to not include synthetic image content, a neural network is applied to the image and the image is classified as one of non-synthetic panoramic or non-synthetic non-panoramic.