Invariant local features, instance recognition, bag-of-words models
- *Object Recognition from Local Scale-Invariant Features, Lowe, ICCV 1999. [pdf] [code] [other implementations of SIFT] [IJCV]
- *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]
- *Video Google: A Text Retrieval Approach to Object Matching in Videos, Sivic and Zisserman, ICCV 2003. [pdf] [demo]
- Scalable Recognition with a Vocabulary Tree, D. Nister and H. Stewenius, CVPR 2006. [pdf]
- SURF: Speeded Up Robust Features, Bay, Ess, Tuytelaars, and Van Gool, CVIU 2008. [pdf] [code]
- Robust
Wide Baseline Stereo from Maximally Stable Extremal Regions, J. Matas, O. Chum, U.
Martin, and T. Pajdla, BMVC 2002. [pdf]
- A Performance Evaluation of Local Descriptors. K. Mikolajczyk and C. Schmid. CVPR 2003 [pdf]
- Oxford group interest point software
- Andrea Vedaldi's code, including SIFT, MSER, hierarchical k-means.
- INRIA LEAR team's software, including interest points, shape features
- Semantic
Robot
Vision
Challenge links
Global appearance models for category and scene recognition, sliding window detection, detection as a binary decision.
- *Histograms of Oriented Gradients for Human
Detection, Dalal and Triggs, CVPR 2005. [pdf]
[video] [code] [PASCAL datasets]
- *Beyond Bags of Features: Spatial Pyramid
Matching for Recognizing Natural Scene Categories, Lazebnik, Schmid,
and Ponce, CVPR 2006. [pdf]
[15
scenes
dataset] [libpmk] [Matlab]
- *Rapid Object Detection Using a Boosted Cascade of Simple Features, Viola and Jones, CVPR 2001. [pdf] [code]
- Modeling the Shape of the Scene: a Holistic
Representation of the Spatial Envelope, Oliva and Torralba, IJCV
2001. [pdf]
[Gist
code]
- 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]
- Pedestrian Detection in Crowded Scenes, Leibe, Seemann, and Schiele, CVPR 2005. [pdf]
- Pyramids of Histograms of Oriented Gradients (pHOG), Bosch and Zisserman. [code]
- Eigenfaces for Recognition, Turk and
Pentland, 1991. [pdf]
- Sampling Strategies for Bag-of-Features Image Classification. E. Nowak, F. Jurie, and B. Triggs. ECCV 2006. [pdf]
- A Trainable System for Object Detection, C. Papageorgiou and T. Poggio, IJCV 2000. [pdf]
- Object Recognition with Features Inspired by Visual
Cortex. T. Serre, L. Wolf and T. Poggio. CVPR
2005.
[pdf]
- LIBPMK feature extraction code, includes dense sampling
- LIBSVM library
for support vector machines
Class begins at 5 pm today.
Part-based models for category recognition, and local feature matching for correspondence-based recognition
- *A Discriminatively Trained, Multiscale,
Deformable Part Model, by P.
Felzenszwalb, D. McAllester and D. Ramanan.
CVPR 2008. [pdf]
[code]
- *Combined Object Categorization and Segmentation with an Implicit Shape Model, by B. Leibe, A. Leonardis, and B. Schiele. ECCV Workshop on Statistical Learning in Computer Vision, 2004. [pdf] [code] [IJCV extended version]
- *Learning a Dense Multi-View Representation
for Detection, Viewpoint Classification and Synthesis of Object
Categories, H. Su, M. Sun, L. Fei-Fei, S. Savarese. ICCV
2009. [pdf]
- Shape Matching and Object Recognition with Low
Distortion Correspondences, A. Berg, T. Berg, and J. Malik, CVPR
2005. [pdf]
[web]
- Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification, Frome, Singer, Sha, Malik. ICCV 2007. [pdf]
- Matching Local Self-Similarities Across Images and Videos, Shechtman and Irani, CVPR 2007. [pdf]
- The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features, Grauman and Darrell. ICCV 2005. [pdf] [web] [code]
- Shape Matching and Object Recognition Using
Shape Contexts. S. Belongie, J. Malik, J. Puzicha. PAMI
2002. [pdf]
- Multiple Component Learning for Object Detection, Dollar, Babenko, Belongie, Perona, and Tu, ECCV 2008. [pdf]
- Object Class Recognition by Unsupervised Scale Invariant Learning, by R. Fergus, P. Perona, and A. Zisserman. CVPR 2003. [pdf] [datasets]
- Efficient Matching of Pictorial Structures. P.
Felzenszwalb
and
D.
Huttenlocher.
CVPR 2000. [pdf]
[related
code]
- A Boundary-Fragment-Model for Object Detection,
Opelt, Pinz, and Zisserman, ECCV 2006. [pdf]
Regions as parts, multi-label segmentation, integrated classification and segmentation
- *Recognition
Using
Regions.
C.
Gu,
J.
Lim,
P.
Arbelaez,
J. Malik, CVPR 2009. [pdf]
[slides]
[seg code]
- *Using
Multiple Segmentations to Discover Objects and their Extent in Image
Collections, B. C. Russell, A. A. Efros, J. Sivic, W. T. Freeman,
and A. Zisserman. CVPR 2006. [pdf] [code]
- *Combining Top-down and Bottom-up Segmentation. E.
Borenstein,
E.
Sharon,
and
S.
Ullman.
CVPR
workshop 2004. [pdf]
[data]
- Extracting Subimages of an Unknown Category from a Set of Images, S. Todorovic and N. Ahuja, CVPR 2006. [pdf]
- Class-Specific, Top-Down Segmentation, E. Borenstein and S. Ullman, ECCV 2002. [pdf]
- Object Recognition by Integrating Multiple Image Segmentations, C. Pantofaru, C. Schmid, and M. Hebert, ECCV 2008 [pdf]
- Image
Parsing: Unifying Segmentation, Detection, and Recognition. Tu, Z., Chen, Z., Yuille,
A.L., Zhu, S.C.
ICCV 2003 [pdf]
- Robust Higher Order Potentials for Enforcing Label Consistency, P. Kohli, L. Ladicky, and P. Torr. CVPR 2008.
- Co-segmentation of Image Pairs by Histogram Matching --Incorporating a Global Constraint into MRFs, C. Rother, V. Kolmogorov, T. Minka, and A. Blake. CVPR 2006. [pdf]
- An Efficient Algorithm for Co-segmentation, D. Hochbaum, V. Singh, ICCV 2009. [pdf]
- Normalized
Cuts
and
Image
Segmentation,
J.
Shi
and
J.
Malik.
PAMI
2000.
[pdf]
[code]
- Greg Mori's superpixel code
- Berkeley Segmentation Dataset and code
- Pedro Felzenszwalb's graph-based segmentation code
- Michael Maire's segmentation code and paper
- Mean-shift: a Robust Approach Towards Feature Space Analysis [pdf] [code, Matlab interface by Shai Bagon]
- David Blei's Topic modeling code
demo: Sudheendra [ppt]
Inter-object relationships, objects within scenes, geometric context, understanding scene layout
- *Discriminative Models for Multi-Class
Object Layout, C. Desai, D. Ramanan, C.
Fowlkes. ICCV 2009. [pdf]
[slides]
[SVM struct code]
[data]
- *TextonBoost: Joint Appearance, Shape and
Context Modeling for Multi-Class Object Recognition and
Segmentation. J. Shotton, J. Winn, C.
Rother, A. Criminisi. ECCV 2006. [pdf] [web] [data]
- *Geometric Context from a Single Image, by
D. Hoiem, A.
Efros, and M. Hebert, ICCV 2005. [pdf]
[web]
[code]
- *Contextual Priming for Object Detection,
A.
Torralba. IJCV 2003. [pdf] [web]
[code]
- Putting Objects in Perspective, by D. Hoiem, A. Efros, and M. Hebert, CVPR 2006. [pdf] [web]
- Decomposing a Scene into Geometric and Semantically Consistent Regions, S. Gould, R. Fulton, and D. Koller, ICCV 2009. [pdf] [slides]
- Learning Spatial Context: Using Stuff to
Find Things, by G.
Heitz and D. Koller, ECCV 2008. [pdf]
[code]
- An Empirical Study of Context in Object
Detection, S. Divvala, D. Hoiem, J. Hays, A. Efros, M. Hebert, CVPR
2009. [pdf]
[web]
- Object Categorization using Co-Occurrence, Location and Appearance, by C. Galleguillos, A. Rabinovich and S. Belongie, CVPR 2008.[ pdf]
- Context Based Object Categorization: A Critical Survey. C. Galleguillos and S. Belongie. [pdf]
- What, Where and Who? Classifying Events by Scene and Object Recognition, L.-J. Li and L. Fei-Fei, ICCV 2007. [pdf]
- Towards Total Scene Understanding: Classification, Annotation and Segmentation in an Unsupervised Framework, L-J. Li, R. Socher, L. Fei-Fei, CVPR 2009. [pdf]
- Labelme Database
- Scene Understanding Symposium
- Stanford STAIR vision
library - includes CRF example
Robert [pdf]
Visual properties, learning from natural language descriptions, intermediate representations
-
Learning Visual Attributes, V. Ferrari and A. Zisserman, NIPS 2007. [pdf]
- Learning Color Names for Real-World Applications, J. van de Weijer, C. Schmid, J. Verbeek, and D. Larlus. IEEE TIP 2009. [pdf] [web]
- Learning Models for Object Recognition from
Natural Language Descriptions, J. Wang, K.
Markert, and M. Everingham, BMVC 2009.[pdf]
Adam [pdf]
Mar 5
Forum for AI Talk
11 AM in ACES 2.302
Mar 8
Recognizing human actions and objects simultaneously, objects and scenes as context for the activity
|
Bottom-up and top-down saliency measures to prioritize features, object importance, saliency in visual search tasks
- *A Model of Saliency-based Visual Attention for
Rapid Scene Analysis. L. Itti, C. Koch, and E. Niebur. PAMI
1998 [pdf]
- *Some Objects are More Equal Than Others: Measuring and Predicting Importance, M. Spain and P. Perona. ECCV 2008. [pdf]
- *Optimal Scanning for Faster Object Detection,
N. Butko, J. Movellan. CVPR 2009. [pdf]
- Reading
Between the Lines: Object Localization Using Implicit Cues from Image
Tags. S. J. Hwang and K. Grauman. CVPR 2010. [pdf]
- Beyond Sliding Windows:
Object Localization by Efficient Subwindow Search, C. Lampert, M.
Blaschko, T. Hofmann. CVPR 2008. [pdf]
- Peripheral-Foveal Vision for Real-time Object Recognition and Tracking in Video. S. Gould, J. Arfvidsson, A. Kaehler, B. Sapp, M. Messner, G. Bradski, P. Baumstrack,S. Chung, A. Ng. IJCAI 2007. [pdf]
- Peekaboom: A Game for Locating Objects in Images, by L. von Ahn, R. Liu and M. Blum, CHI 2006. [pdf] [web]
- Determining Patch Saliency Using Low-Level Context, D. Parikh, L. Zitnick, and T. Chen. ECCV 2008. [pdf]
- Learning to Predict Where Humans Look, T. Judd, K. Ehinger, F. Durand, A. Torralba. ICCV 2009. [pdf] [web]
- Visual
Recognition and Detection Under Bounded Computational Resources, S.
Vijayanarasimhan and A. Kapoor. CVPR 2010.
- Torralba Global
Features
and
Attention
-
The Role of Top-down and Bottom-up Processes
- Amazon Mechanical Turk
- Using
Mechanical Turk with LabelMe
Scalable recognition with many object categories
- *Sharing Visual Features for Multiclass and
Multiview Object Detection, A. Torralba, K. Murphy, W. Freeman, PAMI
2007. [pdf]
[code]
- *Cross-Generalization:
Learning
Novel
Classes
from
a
Single
Example
by
Feature
Replacement.
CVPR
2005.
[pdf]
- *Constructing Category Hierarchies for Visual
Recognition, M. Marszalek and C. Schmid. ECCV 2008. [pdf]
[web] [Caltech256]
- Learning Generative Visual Models from Few Training Examples: an Incremental Bayesian Approach Tested on 101 Object Categories. L. Fei-Fei, R. Fergus, and P. Perona. CVPR Workshop on Generative-Model Based Vision. 2004. [pdf] [Caltech101]
- Towards Scalable Representations of Object
Categories: Learning a Hierarchy of Parts. S.
Fidler and A. Leonardis. CVPR 2007 [pdf]
- Exploiting Object Hierarchy: Combining Models from Different Category Levels, A. Zweig and D. Weinshall, ICCV 2007 [pdf]
- Learning and Using Taxonomies for Fast Visual Categorization, G. Griffin and P. Perona, CVPR 2008. [pdf]
- Incremental Learning of Object Detectors Using a Visual Shape Alphabet. Opelt, Pinz, and Zisserman, CVPR 2006. [pdf]
- Sequential
Learning
of
Reusable
Parts
for
Object
Detection.
S.
Krempp,
D.
Geman,
and
Y.
Amit.
2002
[pdf]
- ImageNet:
A Large-Scale Hierarchical Image Database, J. Deng, W. Dong, R.
Socher, L.-J. Li, K. Li and L. Fei-Fei, CVPR 2009 [pdf]
[data]
Patrick [ppt]
Individual project update meetings (by appt)
Scalable image search with large databases
- *Kernelized Locality Sensitive Hashing for Scalable Image Search, by B. Kulis and K. Grauman, ICCV 2009 [pdf] [code]
- *Geometric Min-Hashing: Finding a (Thick) Needle in a Haystack, O. Chum, M. Perdoch, and J. Matas. CVPR 2009. [pdf]
- *Detecting Objects in Large Image Collections and Videos by Efficient Subimage Retrieval, C. Lampert, ICCV 2009. [pdf] [code] [code]
- 80 Million Tiny Images: A Large Dataset for Non-Parametric Object and Scene Recognition, by A. Torralba, R. Fergus, and W. Freeman. PAMI 2008. [pdf] [web]
- Fast Image Search for Learned Metrics, P.
Jain, B. Kulis, and K. Grauman, CVPR 2008. [pdf]
- Small Codes and Large Image Databases for Recognition, A. Torralba, R. Fergus, and Y. Weiss, CVPR 2008. [pdf]
- Object Retrieval with Large Vocabularies and Fast Spatial Matching. J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, CVPR 2007. [pdf]
- LSH homepage
- Nearest
Neighbor
Methods
in
Learning
and
Vision, Shakhnarovich, Darrell,
and Indyk, editors.
Location recognition, cues from tourist photos, photographer biases, retrieval for landmarks, browsing and visualization
- *Landmark Classification in Large-Scale Image Collections. Y. Li, D. Crandall, D. Huttenlocher. ICCV 2009. [pdf]
- *Image Sequence Geolocation with Human Travel Priors, E. Kalogerakis, O. Vesselova, J. Hays, A. Efros, A. Hertzmann. ICCV 2009. [pdf] [web]
- *Scene Summarization for Online Image Collections. I. Simon, N. Snavely, S. Seitz. ICCV 2007. [pdf] [web]
- Mapping the World's Photos, D. Crandall, L. Backstrom, D. Huttenlocher, J. Kleinberg, WWW 2009. [pdf] [web]
- Im2GPS: Estimating Geographic Information
from a Single Image, J. Hays and A. Efros. CVPR 2008. [pdf]
[web]
- Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval, Chum, Philbin, Sivic, Isard, and Zisserman, ICCV 2007. [pdf]
- Scene Segmentation Using the Wisdom of
Crowds, by I. Simon and S. Seitz. ECCV 2008. [pdf]
- Photo Tourism: Exploring Photo Collections in 3D, by N. Snavely, S. Seitz, and R. Szeliski, SIGGRAPH 2006. [pdf] [web]
- Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs, by X. Li, C. Wu, C. Zach, S. Lazebnik, and J. Frahm, ECCV 2008. [pdf] [web]
- City-Scale Location Recognition, G. Schindler, M. Brown, and R. Szeliski, CVPR 2007. [pdf]
- Parsing Images of Architectural Scenes, A. Berg, F. Grabler, J. Malik. ICCV 2007. [pdf]
- I Know What You Did Last Summer: Object-Level
Auto-annotation of Holiday Snaps, S. Gammeter, L. Bossard, T.Quack, L.
van Gool, ICCV 2009. [pdf]
- CVPR 2009 Workshop on Visual Place Categorization
- Code for downloading Flickr images, by James Hays
- UW Community Photo
Collections homepage
Suyog [pdf]
Discovering the correspondence between words (and other language constructs) to images or video, using captions or subtitles as weak labels.
- *"'Who are you?' - Learning Person Specific
Classifiers from Video, J. Sivic, M.
Everingham, and A. Zisserman, CVPR 2009. [pdf]
- *Beyond Nouns: Exploiting Prepositions and Comparative Adjectives for Learning Visual Classifiers, A. Gupta and L. Davis, ECCV 2008. [pdf]
- *Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary, P. Duygulu, K. Barnard, N. de Freitas, D. Forsyth. ECCV 2002. [pdf] [data]
- The Mathematics of Statistical Machine Translation:
Parameter Estimation. P. Brown, S. Della Pietro, V. Della Pietra,
R. Mercer. Association for Computational Linguistics, 1993.
[pdf]
- Who's Doing What: Joint Modeling of Names and Verbs
for Simultaneous Face and Pose Annotation. L. Jie, B. Caputo, and
V. Ferrari. NIPS 2009. [pdf]
- Names and Faces in the News, by T. Berg, A. Berg, J. Edwards, M. Maire, R. White, Y. Teh, E. Learned-Miller and D. Forsyth, CVPR 2004. [pdf] [web]
- Learning Sign Language by Watching TV (using weakly aligned subtitles), P. Buehler, M. Everingham, and A. Zisserman. CVPR 2009. [pdf] [data]
- “Hello! My name is... Buffy” – Automatic Naming of Characters in TV Video, by M. Everingham, J. Sivic and A. Zisserman, BMVC 2006. [pdf] [web] [data]
- Using Closed Captions to Train Activity
Recognizers that Improve Video Retrieval, S. Gupta and R. Mooney. CVPR
Visual and Contextual Learning Workshop, 2009. [pdf]
- Systematic Evaluation of Machine
Translation Methods for Image and Video Annotation, P. Virga, P.
Duygulu, CIVR 2005. [pdf]
- Subrip for subtitle extraction
- Reuters captioned photos
- Sonal Gupta's data for
commentary+video
Chao-Yeh [ppt]
April 23
Forum for AI Talk
11 AM, TAY 3.128
Faces, consumer photo collections, tagging
- *Understanding Images of Groups of People, A. Gallagher and T. Chen, CVPR 2009. [pdf]
- *Contextual Identity Recognition in Personal Photo Albums. D. Anguelov, K.-C. Lee, S. Burak, Gokturk, and B. Sumengen. CVPR 2007. [pdf]
- *A Face Annotation Framework with Partial Clustering and Interactive Labeling. R. X. Y. Tian,W. Liu, F.Wen, and X. Tang. CVPR 2007. [pdf] [web]
- Autotagging Facebook: Social Network Context Improves Photo Annotation, by Z. Stone, T. Zickler, and T. Darrell. CVPR Internet Vision Workshop 2008. [pdf]
- Efficient Propagation for Face Annotation in Family Albums. L. Zhang, Y. Hu, M. Li, and H. Zhang. MM 2004. [pdf]
- Using Group Prior to Identify People in
Consumer Images, A. Gallagher, T. Chen, CVPR Workshop on Semantic Learning Applications in
Multimedia, 2007. [pdf]
- Leveraging Archival Video for Building Face Datasets, by D. Ramanan, S. Baker, and S. Kakade. ICCV 2007. [pdf]
- Names and Faces in the News, by T. Berg, A. Berg, J. Edwards, M. Maire, R. White, Y. Teh, E. Learned-Miller and D. Forsyth, CVPR 2004. [pdf] [web]
- Face detection code in OpenCV
- Gallagher's Person Dataset
April 30
Project presentations, part I