Research questions – Duplicate detection of images using computer vision techniques

  1. Improving Accuracy in Duplicate Image Detection:
    • How can computer vision techniques, including feature extraction and similarity measures, be enhanced to improve the accuracy of duplicate image detection in large datasets, especially when dealing with images of varying resolutions and qualities?
  2. Scalability and Efficiency:
    • What scalable and efficient computer vision algorithms can be developed to handle the increasing size of image databases, ensuring timely and accurate detection of duplicate images while minimizing computational resources?
  3. Robustness to Image Transformations:
    • How can duplicate detection methods be made robust to common image transformations, such as rotation, scaling, and cropping, ensuring accurate detection even when the same visual content undergoes slight modifications?
  4. Handling Near-Duplicates:
    • What approaches can be devised to effectively identify and manage near-duplicate images, where slight variations or alterations exist, in order to provide a nuanced understanding of visual content similarity?
  5. Cross-Modal Duplicate Detection:
    • How can computer vision techniques be adapted to detect duplicate images across different modalities, such as identifying similar images in both visible and infrared spectrums, or comparing images with accompanying textual descriptions?
  6. Learning-based Duplicate Detection:
    • Can machine learning approaches be effectively employed for duplicate image detection, utilizing deep learning architectures to automatically learn and adapt to complex visual patterns and relationships within image datasets?
  7. Privacy-Preserving Duplicate Detection:
    • What techniques can be employed to perform duplicate detection on images while preserving privacy, especially in scenarios where the images may contain sensitive information, without compromising the accuracy of the detection?
  8. Dynamic Duplicate Detection in Video Streams:
    • How can computer vision techniques be extended to dynamically detect duplicate frames or segments in streaming video content, allowing for real-time identification of redundancy in continuously evolving multimedia data?
  9. Semantic Understanding for Duplicate Detection:
    • How can semantic information and context be integrated into duplicate image detection algorithms, ensuring that visually similar images with different meanings are appropriately distinguished?
  10. Cross-Dataset Duplicate Detection:
    • How can duplicate image detection models be generalized and adapted to different datasets with varying content, styles, and contexts, ensuring their applicability across diverse image collections and domains?