Pietro Perona
California Institute of Technology
433 Papers
4.5K Citations
Pietro Perona is an academic researcher from California Institute of Technology. The author has contributed to research in topics: Computer science & Motion estimation. The author has an hindex of 102, co-authored 414 publications. Previous affiliations of Pietro Perona include Amazon.com & University of California, Berkeley.
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Papers
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
- 06 Sep 2014
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.
Scale-space and edge detection using anisotropic diffusion
Pietro Perona,Jitendra Malik +1 more
TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.
The Caltech-UCSD Birds-200-2011 Dataset
Catherine Wah,Steve Branson,Peter Welinder,Pietro Perona,Serge Belongie +4 more
- 01 Jul 2011
TL;DR: CUB-200-2011 as mentioned in this paper is an extended version of CUB200, which roughly doubles the number of images per category and adds new part localization annotations, annotated with bounding boxes, part locations, and at-ribute labels.
Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories
Li Fei-Fei,Rob Fergus,Pietro Perona +2 more
- 27 Jun 2004
TL;DR: The incremental algorithm is compared experimentally to an earlier batch Bayesian algorithm, as well as to one based on maximum-likelihood, which have comparable classification performance on small training sets, but incremental learning is significantly faster, making real-time learning feasible.
A Bayesian hierarchical model for learning natural scene categories
Li Fei-Fei,Pietro Perona +1 more
- 20 Jun 2005
TL;DR: This work proposes a novel approach to learn and recognize natural scene categories by representing the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning.