Seasonal Image Search via Visual Classification
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Solution Overview
Problem
Current image search systems struggle to identify and retrieve images based on seasonal time periods, especially when metadata about the capture time is absent, limiting users' ability to find images relevant to specific seasonal criteria.
Innovation Solution
The development of an image classification model that predicts the seasonal time period of an input image using visual characteristics, allowing users to filter search results by desired seasonal times, and training the model using time-based and location-based groups to accurately associate images with their capture periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image search systems rely on metadata for time-based filtering, then search accuracy is improved, but the system cannot retrieve images without time information
Solution Approach 1:
The patent introduces an image classification model as an intermediary between the image search system and images lacking time metadata. This model predicts seasonal time periods by analyzing visual characteristics, serving as a mediator that bridges the gap between images with and without time information, enabling time-based filtering across the entire image corpus
Solution Approach 2:
The patent replaces the traditional metadata-based time identification mechanism with a machine learning-based visual analysis system. Instead of relying on mechanical extraction of time data from image files, the system uses trained classification models to infer time information from visual features, substituting a sophisticated computational approach for the limited mechanical metadata extraction
2Measurement precision
If the system processes all images through classification models, then time prediction accuracy is improved, but computational resources increase
Solution Approach 1:
The patent applies classification models selectively rather than uniformly to all images. By training multiple models for different time-based groups (e.g., seasonal periods, times of day) and applying only relevant models to specific images, the system performs partial processing that balances accuracy requirements with computational efficiency, avoiding the excessive energy consumption of universal processing
3Measurement precision
If the system uses multiple classification models for different time groups, then filtering precision is improved, but system complexity increases
Solution Approach 1:
The patent segments the image classification task into multiple specialized models, each trained on specific time-based groups (e.g., different seasons, times of day). This segmentation allows each model to focus on particular temporal patterns, improving filtering precision for specific time periods while organizing complexity into manageable, modular components that can be independently managed and applied
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for retrieving images based on a seasonal time period. In one aspect, a method includes receiving a query including image data defining an image depicting a subject. A location at which the image was captured by an image capturing device is determined. One or more additional images of the subject that were captured by an image capturing device at the identified location are identified. At least a portion of the additional images are provided in response to receiving the query.