Client-Side Digital Image Scene Detection via Compact Models
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Solution Overview
Problem
Conventional digital image analysis systems require significant computational resources, making it difficult for client devices like mobile devices to analyze and classify digital images efficiently, as they need dedicated server devices for processing.
Innovation Solution
The system analyzes digital images by determining object tag probabilities and similarity scores with scene categories, allowing it to identify and classify images in a computationally efficient manner, enabling scene categorization and providing optimized image editing tools on client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional digital image analysis systems are used, then accurate object detection and image classification can be achieved, but enormous computational resources and dedicated server devices are required
Solution Approach 1:
The system segments the image analysis task into two phases: a training phase that runs on server devices to build scene category models, and an inference phase that runs on client devices using compact scene category representations. This segmentation allows heavy computational work to be done once during training, while the lightweight inference model enables accurate classification on resource-constrained devices.
Solution Approach 2:
The system performs preliminary action by pre-training scene category models on server devices and generating compact scene category representations before deployment to client devices. This preliminary processing creates optimized models that can be efficiently executed on mobile devices without requiring enormous computational resources during actual image analysis.
2Adaptability or versatility
If conventional server-based image analysis systems are used, then comprehensive image classification is possible, but client devices are unable to adequately perform digital image analysis
Solution Approach 1:
The system introduces scene category representations as an intermediary between the complex conventional image analysis systems and client devices. These scene category representations serve as a mediator that bridges the gap between comprehensive server-based classification capabilities and the limited processing power of client devices, enabling accurate image classification on mobile devices.
Solution Approach 2:
The system changes parameters by transforming full-scale image analysis models into compact scene category representations with reduced dimensionality and computational requirements. This parameter transformation maintains the essential classification capabilities while adapting the model to run efficiently on resource-constrained client devices.
3Measurement precision
If traditional object detection methods are used, then detailed object recognition is achieved, but the system requires dedicated server devices and cannot run on client devices
Solution Approach 1:
The system extracts the essential scene category classification functionality from the complex conventional image analysis pipeline. By taking out and isolating the core classification task and representing it through compact scene category models, the system enables accurate object recognition to be performed on client devices without requiring the full complexity of dedicated server-based systems.
Data Source
AI summary
The present disclosure includes systems, methods, and computer readable media, that identify one or more scene categories that correspond to digital images. In one or more embodiments, disclosed systems analyze a digital image to determine, for each of a plurality of object tags, a probability that the object tag associates with the digital image. The systems further determine, for each of the plurality of object tags, a similarity score for each of a plurality of scene categories (e.g., a similarity between each object tag and each scene category). Using the object tag probabilities and the similarity scores, the disclosed systems determine a probability, for each scene category, that the digital image pertains to the scene category. Based on the determined probabilities, the disclosed systems are able to identify an appropriate scene category for the digital image.