Technical factors affecting analytical accuracy
The test data shows that when the input photo resolution is lower than 800×600 pixels, the recognition accuracy of Google Vision API will drop from the benchmark value of 92% to 76%; the error rate of recognizing people's emotions in blurred or backlit photos reaches 43%. while the object recognition accuracy of high-definition images captured by a professional DSLR camera can reach 96% and can extract richer metadata parameters. It's worth noting that common social media filter processing can interfere with AI judgment - photos with 'nostalgia' filters added in the test saw a 28% increase in scene recognition error rate.
The developers of the tool recommend that users upload original quality photos for testing, and also offer 3 pre-processing options: auto orientation, intelligent noise reduction and EXIF cleanup. Comparison experiments show that pre-processed low-quality photos can increase the confidence of their analysis results by 19 percentage points. However, the system is still unable to resolve heavily mosaiced or cropped image areas, which is a general limitation of computer vision technology.
This answer comes from the articleThey See Your Photos: Analyzing Photo Privacy Information Based on Google VisionThe
