Multi-stage Image Classification via Segmentation
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Solution Overview
Problem
Existing technologies face challenges in efficiently classifying digital media content due to variations in object appearance, pose, and illumination effects, especially when combining natural photos with computer-generated graphics, leading to inefficient content encoding.
Innovation Solution
A multi-stage image classification method is introduced, comprising a first classification stage that determines an overall classification for an input image using relative entropy, and a second stage that divides the image into blocks for more precise classification based on a specific classification model, allowing for the selection of optimal encoding techniques for each content type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single-stage classification method is used, then the classification process is simple and fast, but the classification accuracy is insufficient due to variations in object appearance, pose, and illumination effects
Solution Approach 1:
The patent divides the classification process into multiple stages: a first classification stage that performs initial classification, and a second classification stage that performs refined classification. This segmentation allows the system to achieve higher accuracy by progressively refining classifications while managing computational complexity through staged processing.
Solution Approach 2:
The first classification stage performs preliminary classification to identify the general category of content. This preliminary action filters out obviously classified content, allowing the second stage to focus computational resources only on ambiguous cases that require more sophisticated analysis.
2Measurement precision
If classification is performed on the entire image, then the overall content type is identified, but the precision is insufficient for mixed content types such as computer-generated graphics combined with natural photos
Solution Approach 1:
The patent segments the image into multiple blocks and performs classification on each block individually in the second classification stage. This allows different content types within the same image (e.g., computer-generated graphics combined with natural photos) to be identified and classified separately, improving precision for mixed content types.
Solution Approach 2:
The patent applies different classification approaches to different regions of the image. The second classification stage uses classification models specific to the overall classification type to analyze individual blocks, allowing local content characteristics to be accurately identified regardless of the overall image category.
3Productivity
If encoding techniques are selected without accurate classification, then the encoding process is fast, but the encoding efficiency and quality are insufficient
Solution Approach 1:
The multi-stage classification process performs preliminary and refined classification before encoding, ensuring that the most accurate content type identification is available. This allows encoding techniques to be selected based on precise classification results, optimizing encoding efficiency and quality without sacrificing speed.
Solution Approach 2:
The patent changes the classification parameters and models between stages. The first stage uses general classification parameters for speed, while the second stage uses more sophisticated parameters and models specific to each overall classification type, achieving both efficiency and accuracy in the encoding selection process.
Data Source
AI summary
Techniques are described for performing multi-stage image classification. For example, multi-stage image classification can comprise a first classification stage and a second classification stage. The first classification stage can determine an overall classification for an input image (e.g., based on a relative entropy result calculated for the input image). The second classification stage can be performed by dividing the image into a plurality of blocks and classifying individual blocks, or groups of blocks, based on a classification model that is specific to the overall classification of the image determined in the first classification stage.