Custom CV Model Improves the Accuracy of Image Theft Detection Solution from 65% to 88%

Our client was looking for a company specializing in Computer Vision services and discovered us via Clutch. Here’s how we devised a solution in two parts.

• 1. Feasibility Study
  • We conducted a 4-week feasibility study to define the solution scope and analyze the platform’s state.
  • Following this, our team worked with client-provided image datasets, filtering, labeling, and organizing them for search optimization.
  • Then, we performed quality checks, including noise removal, pixel adjustments, and correcting misclassified data.
  • Further, our experts enhanced the dataset using techniques like flipping, cropping, blurring, and compressing for better training.
  • As a concluding step, our AI experts studied, preprocessed, and defined approaches for building the image search engine.

1. 2. Development
   • We began by designing a search engine using Computer Vision to find similar images based on user input.
   • Each result displayed a similarity percentage, showing similarity distance for easy data interpretation.
   • Then, we classified results into three buckets: Exact Match, Similar Match, and No Match.
   • Further, we built an image search engine with a five-layered architecture, optimizing performance and accuracy. 
     Here’s what each layer offered:
     • The first layer preprocesses images for noise removal, normalization, filtration, resizing, and grayscale conversion.
     • The second layer extracts image features and generates feature hashes using a custom algorithm.
     • The third layer builds an index tree for faster searching based on feature vectors.
     • The fourth layer calculates similarity distance and matches feature vectors for accurate image scoring.
     • The fifth layer implemented a separate model trained on labeled logo data for logo detection.