Location-Based Image Analysis with CLIP Feature Filtering

The patent divides the image analysis process into multiple independent stages: feature extraction using CLIP model, relevance filtering using autoencoder or Isolation Forest, and location identification using ANN algorithm. Each stage processes specific aspects of the data independently, allowing the system to achieve high accuracy without requiring all components to be complex simultaneously.

The patent applies preliminary filtering actions before final location identification. The autoencoder or Isolation Forest model pre-processes the image features to identify and remove irrelevant content, creating a refined feature set that improves subsequent location matching accuracy. This preliminary action filters out false positives before the final ANN matching stage.

The patent applies partial filtering action through the autoencoder or Isolation Forest stage, which processes only the most relevant image features rather than all pixels and data points exhaustively. This partial action reduces the computational burden while still achieving sufficient accuracy improvement, balancing processing time and precision.

The patent extracts and removes irrelevant features from the image data through the filtering stage, retaining only the most pertinent visual information for location identification. By taking out unnecessary data elements, the system reduces the amount of data that needs to be processed in subsequent stages, thereby reducing overall processing time while maintaining accuracy.

The patent extracts only the most relevant features from the image using the CLIP model's feature extraction capability, rather than processing all image data uniformly. By identifying and extracting only the salient features that matter for location identification, the system reduces the computational resources required for subsequent processing stages.

The CLIP model performs preliminary feature extraction and relevance assessment before the filtering and location identification stages. This preliminary action organizes and pre-ranks features by relevance, allowing subsequent stages to focus computational resources only on the most promising features, thereby reducing overall computational resource consumption.