Video Geolocation via Multi-Feature Classifier Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems face challenges in accurately identifying geographic locations in videos due to lower resolution and visual similarity between different locations, making it difficult to distinguish between urban areas, beaches, and deserts using visual features alone.
Innovation Solution
A classifier training system is developed to infer geographic locations in videos by deriving audiovisual, textual, address, and landmark features, which are used to train classifiers for specific locations, allowing for hierarchical representation and manual or automatic specification of locations, enabling accurate location identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual features alone are used to identify geographic locations in videos, then the system is simple to implement, but the location identification accuracy is low due to lower resolution and visual similarity between different locations
Solution Approach 1:
The patent combines multiple types of features (visual features, audio features, textual metadata, user interaction data) into a unified classification system. The classifier integrates these diverse feature types to accurately identify geographic locations in videos, resolving the contradiction by merging multiple data sources to improve accuracy while managing system complexity through structured feature integration.
Solution Approach 2:
The system employs a universal classifier that can process multiple feature types (visual, audio, textual, interaction-based) and apply them across different location identification tasks. This multi-functional approach allows the same classification framework to handle various location types and video characteristics, improving accuracy without requiring separate specialized systems for each feature type.
2Measurement precision
If multiple feature types are collected and processed to improve location identification accuracy, then the location identification accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary feature extraction and classification in advance, pre-processing visual, audio, and textual features before final location determination. By preparing and organizing these features beforehand, the system reduces real-time computational burden and speeds up the final classification process while maintaining high accuracy through comprehensive feature analysis.
Solution Approach 2:
The patent applies different processing depths and feature extraction methods to different aspects of the video data based on their importance and computational cost. Critical features undergo more intensive analysis while less discriminative features are processed more lightly, optimizing the balance between accuracy and processing time through localized quality adjustment of feature processing.
Data Source
AI summary
A classifier training system trains classifiers for inferring the geographic locations of videos. A number of classifiers are provided, where each classifier corresponds to a particular location and is trained from a training set of videos that have been labeled as representing the location. In one embodiment, the training set is further restricted to those videos in which a landmark matching the location label is detected. The classifier training system extracts, from each of these videos, features that characterize the video, such as audiovisual features, text features, address features, landmark features, and category features. Based on these features, the classifier training system trains a location classifier for the corresponding location.Each of the location classifiers can be applied to videos without associated location labels to predict whether, or how strongly, the video represents the corresponding location. The prediction can be used for a variety of purposes, such as automatic labeling of videos with locations, presentation of location-specific advertisements in association with videos, and display of video data on relevant portions of an electronic map.