Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
- S. Lazebnik, C. Schmid, J. Ponce
- Computer ScienceComputer Vision and Pattern Recognition
- 17 June 2006
This paper presents a method for recognizing scene categories based on approximate global geometric correspondence that exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories.
Online Learning for Matrix Factorization and Sparse Coding
- J. Mairal, F. Bach, J. Ponce, G. Sapiro
- Computer ScienceJournal of machine learning research
- 1 August 2009
A new online optimization algorithm is proposed, based on stochastic approximations, which scales up gracefully to large data sets with millions of training samples, and extends naturally to various matrix factorization formulations, making it suitable for a wide range of learning problems.
Accurate, Dense, and Robust Multiview Stereopsis
- Yasutaka Furukawa, J. Ponce
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 1 August 2010
A novel algorithm for multiview stereopsis that outputs a dense set of small rectangular patches covering the surfaces visible in the images, which outperforms all others submitted so far for four out of the six data sets.
Online dictionary learning for sparse coding
- J. Mairal, F. Bach, J. Ponce, G. Sapiro
- Computer ScienceInternational Conference on Machine Learning
- 14 June 2009
A new online optimization algorithm for dictionary learning is proposed, based on stochastic approximations, which scales up gracefully to large datasets with millions of training samples, and leads to faster performance and better dictionaries than classical batch algorithms for both small and large datasets.
Computer Vision: A Modern Approach
- D. Forsyth, J. Ponce
- Computer Science
- 1 August 2002
Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance and describes numerous important application areas such as image based rendering and digital libraries.
A sparse texture representation using local affine regions
- S. Lazebnik, C. Schmid, J. Ponce
- MathematicsIEEE Transactions on Pattern Analysis and Machine…
- 1 August 2005
The proposed texture representation is evaluated in retrieval and classification tasks using the entire Brodatz database and a publicly available collection of 1,000 photographs of textured surfaces taken from different viewpoints.
Non-local sparse models for image restoration
- J. Mairal, F. Bach, J. Ponce, G. Sapiro, Andrew Zisserman
- Computer ScienceIEEE International Conference on Computer Vision
- 1 September 2009
Experimental results in image denoising and demosaicking tasks with synthetic and real noise show that the proposed method outperforms the state of the art, making it possible to effectively restore raw images from digital cameras at a reasonable speed and memory cost.
Accurate, Dense, and Robust Multi-View Stereopsis
- Yasutaka Furukawa, J. Ponce
- Computer ScienceIEEE Conference on Computer Vision and Pattern…
- 17 June 2007
A novel algorithm for calibrated multi-view stereopsis that outputs a (quasi) dense set of rectangular patches covering the surfaces visible in the input images, which is currently the top performer in terms of both coverage and accuracy for four of the six benchmark datasets presented in [20].
Task-Driven Dictionary Learning
- J. Mairal, F. Bach, J. Ponce
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 27 September 2010
This paper presents a general formulation for supervised dictionary learning adapted to a wide variety of tasks, and presents an efficient algorithm for solving the corresponding optimization problem.
Learning mid-level features for recognition
- Y-Lan Boureau, F. Bach, Yann LeCun, J. Ponce
- Computer ScienceIEEE Computer Society Conference on Computer…
- 13 June 2010
This work seeks to establish the relative importance of each step of mid-level feature extraction through a comprehensive cross evaluation of several types of coding modules and pooling schemes and shows how to improve the best performing coding scheme by learning a supervised discriminative dictionary for sparse coding.
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