Media Intelligence Automation for Advertisement Segmentation
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Solution Overview
Problem
Existing image segmentation methods fail to accurately distinguish between advertisements and non-advertisement content in multimedia materials, particularly when advertisements are embedded within articles or videos, and struggle with determining the boundaries between adjacent advertisements, leading to inadequate segmentation reliability.
Innovation Solution
The use of advanced machine learning and AI technologies, combined with Optical Character Recognition (OCR), to segment, detect, and annotate multimedia content, including proprietary functional blocks that improve image segmentation by identifying borders and analyzing attributes like font size, logo size, and relative positioning, and the implementation of a dynamic annotation scheme with semantic information organization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing image segmentation methods are used, then the process is simple and fast, but the accuracy of distinguishing advertisements from non-advertisement content deteriorates
Solution Approach 1:
The system segments multimedia content into distinct regions using multiple segmentation methods (contour-based, threshold-based, region-based) to separate advertisement content from non-advertisement content. This enables accurate classification by analyzing boundaries and characteristics of segmented regions independently
Solution Approach 2:
The system implements a multi-functional segmentation framework that combines multiple segmentation algorithms and analysis methods within a single platform. This universal system can handle various types of multimedia content (images, videos, articles) and applies different segmentation strategies based on content characteristics
2Measurement precision
If advanced machine learning and AI technologies are used, then the accuracy of advertisement classification improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary segmentation using faster contour-based and threshold-based methods before applying more computationally intensive machine learning algorithms. This preliminary action reduces the complexity of subsequent processing by pre-identifying potential advertisement regions
Solution Approach 2:
The system applies machine learning algorithms selectively to regions identified as potential advertisements through preliminary segmentation, rather than processing the entire multimedia content. This partial action approach maintains high accuracy while reducing overall processing time
3Reliability
If multiple segmentation methods are combined, then the reliability of advertisement identification improves, but the system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where results from different segmentation methods are cross-validated and compared. Discrepancies between methods trigger additional analysis or adjustment, ensuring reliable identification of advertisement content while managing system complexity through structured decision-making
Data Source
AI summary
Systems and methods for analyzing, segmenting, and classifying multimedia material are disclosed herein. Embodiments include (i) receiving multimedia material for analysis, (ii) extracting elements from the multimedia material and forming objects comprising the elements; (iii) segmenting the multimedia material into segments, where individual segments include objects located within a threshold distance from each other; (iv) detecting objects within each segment; (v) associating attributes with the detected objects within the segments; (vi) annotating the segments by creating a relationship tree among the objects within each segment; and (vii) storing annotations of the segments for analysis.