图像分类之特征学习ECCV-2010 Tutorial: Feature Learning for Image Classification

Posted

tags:

篇首语:本文由小常识网(cha138.com)小编为大家整理,主要介绍了图像分类之特征学习ECCV-2010 Tutorial: Feature Learning for Image Classification相关的知识,希望对你有一定的参考价值。

ECCV-2010 Tutorial: Feature Learning for Image Classification

 

Organizers

Kai Yu (NEC Laboratories America, [email protected]),

Andrew Ng (Stanford University, [email protected])

Place & Time: Creta Maris Hotel, Crete, Greece, 9:00 – 13:00, September 5th, 2010

Course Material and Software

The quality of visual features is crucial for a wide range of computer vision topics, e.g., scene classification, object recognition, and object detection, which are very popular in recent computer vision venues. All these image classification tasks have traditionally relied on hand-crafted features to try to capture the essence of different visual patterns. Fundamentally, a long-term goal in AI research is to build intelligent systems that can automatically learn meaningful feature representations from a massive amount of image data. We believe a comprehensive coverage of the latest advances on image feature learning will be of broad interest to ECCV attendees.

The primary objective of this tutorial is to introduce a paradigm of feature learning from unlabeled images, with an emphasis on applications to supervised image classification. We provide a comprehensive coverage of recently developed algorithms for learning powerful sparse nonlinear features, and showcase their superior performance on a number of challenging image classification benchmarks, including Caltech101, PASCAL, and the recent large-scale problem ImageNet. Furthermore, we describe deep learning and a variety of deep learning algorithms, which learn rich feature hierarchies from unlabeled data and can capture complex invariance in visual patterns.

Syllabus

 

  • Overview: Image Classification Overview
  • Part I: State-of-the-art Image Classification Methods
    • Discriminative Classifiers using BoW Representation and Spatial Pyramid Matching
    • Alternative Methods: Generative Models and Part-based Models
  • Part II: Image Classification using Sparse Coding
    • Self-taught Learning
    • BoW Representation from a Coding Perspective
    • Feature Learning using Sparse Coding
    • Alternative Sparse Coding Methods: Sparse RBM, Sparse Autoencoder, etc.
  • Part III: Advanced Topics on Image Classification using Sparse Coding
    • Intuitions, Topic-model View, and Geometric View
    • Local Coordinate Coding: Theory and Applications
    • Recent Advances in Sparse Coding for Image Classification
  • Part IV: Learning Feature Hierarchies and Deep Learning
    • Feature Hierarchies and the Importance of Depth
    • Deep Belief Networks (DBNs) and Convolution DBNs
    • Learning Invariance (ICA, SFA, etc.)
    • Other Deep Architectures
    • Application to Image Classification
  • Open questions and discussion

 

Course Material and Software

 

The slides:

 

Software available online:

  • Matlab toolbox for sparse coding using the feature-sign algorithm [link]
  • Matlab codes for image classification using sparse coding on SIFT features [link]
  • Matlab codes for a fast approximation to Local Coordinate Coding [link]

 

Relevant Tutorials

 

 

Biographies

Kai Yu is a Department Head at NEC Labs America, where he leads the research in image understanding, video surveillance, and data mining. He served as Session Chair at ICML 2009 and Area Chair at ICML 2010, and received the best paper runner-up award in PKDD-05. His team won the Winner Prizes in PASCAL VOC Challenge 2009 and the ImageNet Large-scale Visual Recognition Challenge 2010, and was among the top performers in TRECVID Video Event Detection Evaluations in 2008 and 2009. He received Ph.D in CS from University of Munich, Germany, in 2004.

Andrew Ng is an Associate Professor of Computer Science at Stanford University. His research interests include machine learning, robotics, and broad-competence AI. His group has won best paper/best student paper awards at ACL, CEAS, 3DRR and ICML. He is also a recipient of the Alfred P. Sloan Fellowship, and the IJCAI 2009 Computers and Thought award.

from: http://ufldl.stanford.edu/eccv10-tutorial/

以上是关于图像分类之特征学习ECCV-2010 Tutorial: Feature Learning for Image Classification的主要内容,如果未能解决你的问题,请参考以下文章

全球名校课程作业分享系列--斯坦福计算机视觉与深度学习CS231n之特征抽取与图像分类提升

软件工程学习进度第八周暨暑期学习进度之第八周汇总

多模态特征融合:图像语音文本如何转为特征向量并进行分类

深度学习之图像分类ResNet50学习

Opencv学习之路—Opencv下基于HOG特征的KNN算法分类训练

支持向量机算法之鸢尾花特征分类机器学习