Repetitive Structure Extraction for Arbitrary Time Image Search
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Solution Overview
Problem
Existing image search methods struggle to handle image changes over various lengths of time at specific locations, limiting their ability to search for images corresponding to arbitrary times, especially in cases where images span multiple time zones or have varying capture times.
Innovation Solution
A device and method that extract image features and temporal features from images captured at different times, learning a repetitive structure to interconvert between these features based on periodic changes, enabling the estimation of image features at arbitrary times and the search for matching images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If image search methods limit the time length to simplify complex changes, then the system complexity is reduced, but the ability to handle image changes over various lengths of time is lost
Solution Approach 1:
The patent segments the temporal dimension by extracting multiple temporal features corresponding to different time lengths (e.g., daily, weekly, monthly patterns). Each temporal feature represents a specific periodicity, allowing the system to handle image changes at various time scales without creating a single complex unified model. This segmentation enables independent processing and learning of different temporal patterns.
Solution Approach 2:
The patent introduces a temporal feature dimension that complements the traditional image feature dimension. By extracting temporal features that represent periodic changes at different time lengths and combining them with image features through a learned mapping relationship, the system transforms the problem from handling temporal variations in a single dimension to managing multiple independent temporal dimensions, thereby reducing overall system complexity while maintaining versatility.
2Adaptability or versatility
If the system extracts multiple temporal features for different time lengths, then the ability to search for images at arbitrary times is improved, but the device complexity increases
Solution Approach 1:
The patent creates a universal temporal feature extraction mechanism that can handle multiple time lengths through a single framework. The temporal feature extraction unit extracts features for different time lengths using the same basic processing logic, and the learned mapping relationship between temporal features and image features serves multiple purposes: it enables image search at arbitrary times, supports capture time estimation, and handles various periodic patterns. This multi-functionality reduces the need for separate specialized components for each time length.
Solution Approach 2:
The patent changes the parameter of time length flexibility by learning a mapping relationship that connects temporal features at different time lengths with image features. Instead of requiring separate processing pipelines for different time periods, the system learns a unified transformation that can accommodate various time lengths, thereby reducing device complexity while maintaining the ability to search for images at arbitrary times.
3Measurement precision
If the system learns repetitive structures from images with accurate capture times, then the precision of time-based image search is improved, but the system cannot handle images with erroneous or missing auxiliary information
Solution Approach 1:
The patent enables the system to self-correct by learning the repetitive structure from images that have accurate auxiliary information. The learned mapping relationship between temporal features and image features allows the system to infer and correct erroneous or missing auxiliary information. When processing images with inaccurate or missing metadata, the system uses the learned relationship to automatically adjust and recover the intended temporal characteristics, thereby maintaining high search precision without requiring perfectly accurate input data.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously refines its understanding of temporal patterns by comparing predicted image features (based on learned repetitive structures) with actual observed features. This feedback loop enables the system to detect and correct deviations caused by erroneous or missing auxiliary information, gradually improving its ability to handle such cases while maintaining high measurement precision for images with accurate metadata.
Data Source
AI summary
A repetitive structure extraction device includes an image feature extraction unit which extracts an image feature for each of a plurality of images which are captured at one or a plurality of locations and which are given different capture times, a temporal feature extraction unit which extracts, for each of the plurality of images, a temporal feature according to a predetermined period from a capture time given to the image, and a repetitive structure extraction unit which learns, on the basis of the image feature extracted for each of the plurality of images by the image feature extraction unit and the temporal feature extracted for each of the plurality of images by the temporal feature extraction unit, a repetitive structure which is used to perform interconversion between the temporal feature and a component of the image feature and which is provided according to a correlation of periodic change between the component of the image feature and the temporal feature.