Project E/R on Content-Based Image Retrieval

Brief introduction to the research problem

The project has the aim of studying and implementing algorithms of computer vision, machine learning and pattern recognition for the analysis of large datasets of images.

The final target is to accurately and automatically identify places and buildings (landmarks) in urban or industrial environment.

Example of CBIR application

There are many drawbacks, that make the task of matching features between a query image and the database rather difficult:

  • changes in the image resolution, illumination conditions, viewpoint;
  • occlusions;
  • presence of distractors such as trees or traffic signs.

Instead, the motivations, that make the problem interesting are:

  • obtain an high accuracy in the retrieval phase;
  • execute a fast research;
  • reduced use of memory (mobile friendly);
  • work well with big data (dataset size > 1M).

Publications

    • A Location-Aware Embedding Technique for Accurate Landmark Recognition
      International Conference on Distributed Smart Cameras (ICDSC), 2017 [paper] [project] [slides]
    • An Accurate Retrieval through R-MAC+ Descriptors for Landmark Recognition
      International Conference on Distributed Smart Cameras (ICDSC), 2018 [paper] [slides] [code]
    • Efficient Nearest Neighbors Search for Large-Scale Landmark Recognition 
      International Symposium on Visual Computing (ISVC), 2018
      [paper] [poster] [slides] [code]
    • A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval
      International Symposium on Visual Computing (ISVC), 2018 [paper] [slides]
    • Landmark Recognition: From Small-Scale to Large-Scale Retrieval 
      Recent Advances in Computer Vision (Springer), 2018 [book]
    • An Efficient Approximate kNN Graph Method for Diffusion on Image Retrieval ,
      International Conference on Image Analysis and Processing (ICIAP), 2019 [paper][code]
    • Genetic Algorithms for the Optimization of Diffusion Parameters in Content-Based Image Retrieval ,
      International Conference on Distributed Smart Cameras (ICDSC), 2019 [paper]

The project is totally funded by the italian region Emila Romagna.

Federico Magliani is the supervisor for this project.