Recognition and Classification in Images and Video *
203.4780
Course overview
Useful links
Syllabus Detailed schedule
Meeting Times: Monday 9-12, Room 704
Instruction Hour: Wednesday 11:00-12:00, Room 410 (Jacobs)
Instructor: Dr.
Rita Osadchy
e-mail: rita [at]cs
[dot]haifa.ac.il
Office: Jacobs 410
Announcements:
§ NEW:
Final Project submission is due to 12.10
§ 30.03 no
class
§ All announcements and guidelines
will be distributed by email.
§ Those who do not send their contact
address on time will not be added to the contact list!!!
§ You must send me an email to (rita[at]cs[dot]haifa.ac.il)
by March 15 from your active address with the subject "course
4780"
Course overview:
General: This is a graduate
course in computer vision. We will survey and discuss vision papers
relating to object and activity recognition and scene understanding. The
goal of the course is to understand classical and modern approaches to some
important problems, analyzing their strengths and weaknesses, and identifying
interesting open questions.
Requirements: Students will be responsible for writing a paper review each week, participating in discussions, completing a programming project, and presenting one topic in a class.
Note that presentations are due one week before the slot your presentation is scheduled. This means you will need to read the papers, create slides, etc. one week before the date you are signed up for, to leave time for improvement. Note, that you should get my approval for your presentation.
More details on the requirements and grading breakdown are here.
A. Recognizing specific objects
Global features:
1. Linear Subspaces
2. Detection
as a binary decision
Local features:
3. Local features, matching for object
instances
4. Visual Vocabularies and Bag of Words
Region-based methods:
5. Mid-Level Representations
B. Beyond Single objects (using
additional information)
1. Saliency
2. Attributes
3. Context
C. Scalability problems
1. Scaling with the large number of
categories
D. Action recognition in video and
images
Schedule and papers:
Note: * = required reading.
Additional papers are provided for reference, and as a starting point for
background reading for projects.
Paper presentations: Cover the starred papers.
|
Date |
Topics |
Papers
and links |
Presenters |
||||||
|
16.3 |
Course
intro |
[slides] |
|||||||
|
23.3 |
Introduction
to Object and Event Recognition |
[slides] |
|
||||||
|
30.3 |
No class |
|
|
||||||
|
Linear Subspaces Global appearance models for object recognition, dimensionality reduction.
|
o
*Eigenfaces
for Recognition, Turk and Pentland, 1991. [pdf] o
*P.N. Belhumeur,
J.P. Hespanha, D.J. Kriegman,
Eigenfaces vs. Fisherfaces:
Recognition using Class Specific Linear Projection, 1996 [pdf] o
Face Database [here]
|
Magali Nadav [pdf] |
|||||||
|
20.4 |
Local features and matching for object
instances:
|
o
*Object Recognition from Local
Scale-Invariant Features, Lowe, ICCV 1999. [pdf] [code] [other
implementations of SIFT] [IJCV] o
*Selected pages from: Local
Invariant Feature Detectors: A Survey, Tuytelaars
and Mikolajczyk. Foundations and Trends in
Computer Graphics and Vision, 2008. [pdf]
[Oxford code]
[Read pp. 178-188, 216-220, 254-255] o
o
Oxford group interest point software o
Andrea Vedaldi's VLFeat code,
including SIFT, MSER, hierarchical k-means. o
INRIA LEAR team's software, including interest
points, shape features o
FLANN - Fast
Library for Approximate Nearest Neighbors. Marius Muja et al. o
Kooaba
|
שון מן שמעון פאץ |
||||||
|
27.4 |
Patch-based
Representations visual
vocabularies, bag-of-words and SPK for scene classification
|
o
*Visual Categorization with Bags of Keypoints, C. Dance, J. Willamowski,
L. Fan, C. Bray, and G. Csurka, ECCV International
Workshop on Statistical Learning in Computer Vision, 2004. [pdf] o
*Beyond Bags of Features: Spatial
Pyramid Matching for Recognizing Natural Scene Categories, Lazebnik, Schmid, and Ponce,
CVPR [pdf],
[code],[data].
|
Orr Zilberman
and Ran Bakalo [pdf] |
||||||
|
4.5 |
Detection as a
binary decision Sliding window detection, detection as a binary decision
problem.
|
o
*Histograms of Oriented Gradients
for Human Detection, Dalal and Triggs,
CVPR 2005. [pdf]
[code] [PASCAL datasets] o
*Rapid Object Detection Using a
Boosted Cascade of Simple Features, Viola and Jones, CVPR 2001. [pdf]
[code] o
LIBSVM library for
support vector machines o
PASCAL
VOC Visual Object Classes Challenge
|
דוד,
טל [pdf] |
||||||
|
Importance
and saliency
|
|
||||||||
|
o *Describing Objects by Their Attributes, A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth, CVPR 2009. [pdf] [web and data] o *Attribute and Simile Classifiers for Face Verification, N. Kumar, A. Berg, P. Belhumeur, S. Nayar. ICCV 2009 [pdf] [web] [lfw data] [pubfig |
data]
|
Relative
Attributes. D. Parikh and K. Grauman. ICCV 2011. [pdf] [code/data] |
