Search Results for author: Federico Magliani

Found 6 papers, 3 papers with code

Genetic Algorithms for the Optimization of Diffusion Parameters in Content-Based Image Retrieval

no code implementations19 Aug 2019 Federico Magliani, Laura Sani, Stefano Cagnoni, Andrea Prati

We propose to use genetic algorithms to find the optimal setting of all the diffusion parameters with respect to retrieval performance for each different dataset.

Content-Based Image Retrieval

An Efficient Approximate kNN Graph Method for Diffusion on Image Retrieval

1 code implementation18 Apr 2019 Federico Magliani, Kevin McGuinness, Eva Mohedano, Andrea Prati

The application of the diffusion in many computer vision and artificial intelligence projects has been shown to give excellent improvements in performance.

Image Retrieval

A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval

no code implementations15 Aug 2018 Federico Magliani, Tomaso Fontanini, Andrea Prati

The recent advances brought by deep learning allowed to improve the performance in image retrieval tasks.

Content-Based Image Retrieval

An accurate retrieval through R-MAC+ descriptors for landmark recognition

1 code implementation22 Jun 2018 Federico Magliani, Andrea Prati

The landmark recognition problem is far from being solved, but with the use of features extracted from intermediate layers of Convolutional Neural Networks (CNNs), excellent results have been obtained.

Landmark Recognition

Efficient Nearest Neighbors Search for Large-Scale Landmark Recognition

1 code implementation15 Jun 2018 Federico Magliani, Tomaso Fontanini, Andrea Prati

It allows to drastically reduce the query time and outperforms the accuracy results compared to the state-of-the-art methods for large-scale landmark recognition.

Landmark Recognition

A location-aware embedding technique for accurate landmark recognition

no code implementations19 Apr 2017 Federico Magliani, Navid Mahmoudian Bidgoli, Andrea Prati

The current state of the research in landmark recognition highlights the good accuracy which can be achieved by embedding techniques, such as Fisher vector and VLAD.

Landmark Recognition

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