Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach

no code implementations • 1 Mar 2016 • Mattis Paulin, Julien Mairal, Matthijs Douze, Zaid Harchaoui, Florent Perronnin, Cordelia Schmid

Convolutional neural networks (CNNs) have recently received a lot of attention due to their ability to model local stationary structures in natural images in a multi-scale fashion, when learning all model parameters with supervision.

Image Classification Image Retrieval +1

Local Convolutional Features With Unsupervised Training for Image Retrieval

no code implementations • ICCV 2015 • Mattis Paulin, Matthijs Douze, Zaid Harchaoui, Julien Mairal, Florent Perronin, Cordelia Schmid

Patch-level descriptors underlie several important computer vision tasks, such as stereo-matching or content-based image retrieval.

Content-Based Image Retrieval Retrieval +2

Transformation Pursuit for Image Classification

no code implementations • CVPR 2014 • Mattis Paulin, Jerome Revaud, Zaid Harchaoui, Florent Perronnin, Cordelia Schmid

We propose a principled algorithm – Image Transformation Pursuit (ITP) – for the automatic selection of a compact set of transformations.

Classification General Classification +1