Last night we had the 7th Paris Machine Learning Meetup at DojoEvents. We first had two Skype presentations of crowdfunded projects that are connected to Machine Vision. The Q&As are in English and before watching the video above you want to watch the VMX video introduction which is what the attendees saw before the Hangout started. It is here.
- Tomasz Malisiewicz, VMX Project: Computer Vision for Everyone (English)
- Allen Yang, Atheer One, what it feels like to have superpowers (English)
- Patrick Perez From image to descriptors and back again (French)
- Kenji Lefèvre-Hasegawa , 'Dataiku Science Studio', What does it take to win the Kaggle/Yandex competition ? (French)
From image to descriptors and back again , Patrick Perez
The context of this presentation is visual search, with a specific focus on retrieving images similar to a query image. I will first discuss one corner stone of such large-scale systems: aggregation of local descriptors (typically SIFTs) into a fixed-sized image signature. I shall present "Vector of Locally Aggregated Descriptors" (VLAD) which offers a powerful alternative to popular "Bag-of-Word" (BoF) approach. Combined with an efficient indexing system, VLAD can lead to a memory footprint as compact as 16 bytes per image with good approximate search performance. I will then touch upon risks of visual information leakage in such image search systems, showing that human-understandable reconstruction of an image can be obtained from the sparse set of its local descriptors, and no other side information.
What does it take to win the Kaggle/Yandex competition, Kenji Lefèvre-Hasegawa
I'll give a feedback on the "personnalized web search" Kaggle challenge for which our team won First prize. I will focus the talk on both the experience itself (team work and tools) and the models we've used.
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.