Server Image Analysis with Multiple Models
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Solution Overview
Problem
Current AI systems lack the ability to provide diverse and reliable image analysis services, as they often rely on a single analysis model, limiting the types of image analysis information that can be provided and making them less effective in responding to user inquiries without specific information.
Innovation Solution
A server system that includes a communicator and processor configured to receive images, apply multiple image analysis models with different analysis types, generate analysis result information, and store analysis results, allowing for various types of image analysis information to be provided, even when user inquiries are not specific.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single image analysis model is used, then the system is simple and easy to operate, but the diversity and reliability of image analysis services are limited
Solution Approach 1:
The patent divides the image analysis function into multiple independent analysis models, each specializing in different analysis types (e.g., object recognition, scene understanding, image enhancement). This segmentation allows the system to provide diverse analysis services while keeping each individual model relatively simple and manageable.
Solution Approach 2:
The server is designed with multi-functionality by integrating multiple image analysis models that can handle various types of image analysis tasks. This universal approach enables a single system to provide diverse services including object recognition, scene understanding, and image enhancement, resolving the contradiction between versatility and complexity.
2Reliability
If multiple image analysis models are applied, then the reliability and diversity of analysis results improve, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary classification of incoming images to determine which analysis models are most appropriate for each image. This preliminary action allows the server to apply only the necessary analysis models rather than all available models, thereby improving reliability through selective multi-model application while reducing processing time.
Solution Approach 2:
The patent implements a balanced approach where multiple analysis models are applied, but not all models are executed for every image. Instead, the system applies a subset of models based on image characteristics and service requirements, achieving sufficient reliability without the excessive processing time that would result from applying all possible models to every image.
3Adaptability or versatility
If multiple image analysis models are used, then various types of image analysis information can be provided, but the device complexity and operational difficulty increase
Solution Approach 1:
The system automatically selects and applies appropriate analysis models based on image characteristics and service requirements without requiring user intervention. This self-service mechanism allows the server to provide diverse image analysis information while maintaining ease of operation, as users simply need to submit images without needing to understand or configure which analysis models to use.
Solution Approach 2:
The server is designed as a universal platform that handles multiple types of image analysis through integrated models. This multi-functionality is presented through a unified interface, allowing users to access diverse analysis services without dealing with the underlying complexity of multiple models, thus maintaining ease of operation while providing versatility.
Data Source
AI summary
A server and a control method thereof are provided. The server includes a communicator configured to communicate with an external apparatus; and a processor configured to: receive an image from the external apparatus via the communicator, process the received image by applying a plurality of image analysis models of which an analysis type for the image is different from each other, to the received image, and generate analysis result information about the image respectively corresponding to a plurality of analysis types according to the processing of the received image. With this, more various types of image analysis information may be provided with respect to one image. At least a portion of the analysis of the image, the processing and the generation may be carried out using at least one of a machine learning, a nerve network or a deep learning algorithm as a rule based or artificial intelligence algorithm.