Bi-Level Content Annotation Using Artificial Neural Networks
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Solution Overview
Problem
Manual annotation of video content is labor-intensive and time-consuming, necessitating an automated solution to minimize the need for human processing.
Innovation Solution
The use of an artificial neural network (ANN), specifically a convolutional neural network (CNN), to perform bi-level specificity content annotation, automating the tagging process by identifying generic and specific content features within video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used, then annotation accuracy can be maintained, but labor intensity and time consumption increase significantly
Solution Approach 1:
The patent introduces an artificial neural network as an intermediary between the video content and the annotation process. The ANN automatically generates annotations by learning from training data, serving as a mediator that reduces direct human involvement while maintaining annotation quality through automated pattern recognition and classification.
2Productivity
If automated annotation using ANN is implemented, then productivity increases, but system complexity increases
Solution Approach 1:
The annotation system is segmented into distinct functional modules: a training module that processes training data to generate training annotations, and an annotation module that applies the trained ANN to generate final annotations. This segmentation allows the complex ANN system to be managed through separate, specialized components with clearly defined responsibilities.
3Loss of information
If bi-level specificity annotation is performed, then annotation detail and usefulness improve, but processing complexity increases
Solution Approach 1:
The patent adds a dimension of specificity by implementing bi-level annotation that captures both generic content categories and specific content features. This dimensional expansion allows annotations to operate at multiple levels of detail simultaneously, providing both broad classification and fine-grained identification without requiring separate processing systems.
Data Source
AI summary
A content annotation system includes a computing platform having a hardware processor and a memory storing a tagging software code including an artificial neural network (ANN). The hardware processor executes the tagging software code to receive content having a content interval including an image of a generic content feature, encode the image into a latent vector representation of the image using an encoder of the ANN, and use a first decoder of the ANN to generate a first tag describing the generic content feature based on the latent vector representation. When a specific content feature learned by the ANN corresponds to the generic content feature described by the first tag, the tagging software code uses a second decoder of the ANN to generate a second tag uniquely identifying the specific content feature based on the latent vector representation, and tags the content interval with the first and second tags.