14.5.4 Learning Object Descriptions, Object Recognition

Chapter Contents (Back)
Object Descriptions. Learning. See also Feature, Object, Blob Detection and Spot Detection Systems.

Cohen, B.[Brian], Sammut, C.[Claude],
Object recognition and concept learning with CONFUCIUS,
PR(15), No. 4, 1982, pp. 309-316.
Elsevier DOI 0309
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Soucke, B.[Branko],
Fast Learning and Invariant Object Recognition: The Sixth Generation Breakthrough,
New York: Wiley-Interscience1992. ISBN 0-471-57430-9. BibRef 9200

Chan, T.Y.T.[Tony Y.T.], Goldfarb, L.[Lev],
Primitive pattern learning,
PR(25), No. 8, August 1992, pp. 883-889.
Elsevier DOI 0401
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Edelman, S.,
On Learning to Recognize 3-D Objects from Examples,
PAMI(15), No. 8, August 1993, pp. 833-837.
IEEE DOI BibRef 9308

Edelman, S.,
Representation of Similarity in 3-Dimensional Object Discrimination,
NeurComp(7), No. 2, March 1995, pp. 408-423. BibRef 9503

Edelman, S.[Shimon],
Representing 3D Objects by Sets of Activities of Receptive Fields,
BioCyber(70), 1993, pp. 37-45. BibRef 9300

Edelman, S.[Shimon],
Viewpoint-specific Representations in Three-dimensional Object Recognition,
MIT AI Memo-1239, August 1990. BibRef 9008

Edelman, S.,
Class Similarity and Viewpoint Invariance in the Recognition of 3D Objects,
BioCyber(72), No. 3, February 1995, pp. 207-220. BibRef 9502

Edelman, S.[Shimon], and Weinshall, D.[Daphna],
A Self-Organizing Multiple-View Representation of 3D objects,
BioCyber(64), 1991, pp. 209-219. BibRef 9100

Edelman, S.[Shimon], Bulthoff, H.H.[Heinrich H.],
Orientation Dependence in the Recognition of Familiar and Novel Views of 3D Objects,
Vision Research(32), 1992, pp. 2385-2400. BibRef 9200

Cutzu, F.[Florin], Edelman, S.[Shimon],
Canonical Views in Object Representation and Recognition,
Vision Research(34), 1994, pp. 3037-3056. BibRef 9400

Poggio, T.[Tomaso], and Sung, K.K.[Kah-Kay],
Networks that Learn for Image Understanding,
AIU96(226-240). BibRef 9600

Sung, K.K.[Kah-Kay],
Learning and Example Selection for Object and Pattern Detection,
MIT AI-TR-1572, January 1996.
WWW Link. BibRef 9601

Poggio, T., Edelman, S.,
A Network that Learns to Recognize 3D Objects,
Nature(343), No. 6255, 1990, pp. 263-266. BibRef 9000

Edelman, S., Poggio, T.,
Representations in High-Level Vision: Reassessing the Inverse Optics Paradigm,
DARPA89(944-949). BibRef 8900

Maloof, M.A., Michalski, R.S.,
Learning Symbolic Descriptions of Shape for Object Recognition in X-Ray Images,
ExSysApp(12), No. 1, 1997, pp. 11-20. 9701
BibRef

Lee, C.M., Pong, T.C., Esterline, A., Slagle, J.R.,
KOR: A Knowledge-Based Object Recognition System,
CVIP92(329-362). BibRef 9200

Lee, C.M.[Chung-Mong], Pong, T.C.[Ting-Chuen], Slagle, J.R.[James R.], Esterline, A.[Albert],
An Experimental-Study of an Object Recognition System That Learns,
PR(27), No. 1, January 1994, pp. 65-89.
Elsevier DOI BibRef 9401

Epstein, R., Yuille, A.L., Belhumeur, P.N.,
Learning Object Representations from Lighting Variations,
ORCV96(179) 9611
BibRef

Sugihara, K.[Kokichi],
A graph-theoretical method for monitoring concept formation,
PR(28), No. 11, November 1995, pp. 1635-1643.
Elsevier DOI 0401
Human learning of concepts. BibRef

Wallis, G., Baddeley, R.,
Optimal, Unsupervised Learning in Invariant Object Recognition,
NeurComp(9), No. 4, May 15 1997, pp. 883-894. 9706
BibRef

Peng, J.[Jing], and Bhanu, B.[Bir],
Close-Loop Object Recognition Using Reinforcement Learning,
PAMI(20), No. 2, February 1998, pp. 139-154.
IEEE DOI 9803
BibRef
Earlier: CVPR96(538-543).
IEEE DOI BibRef
And: A2, A1: ARPA94(I:777-780). BibRef

Peng, J.[Jing], Bhanu, B.[Bir],
Local discriminative learning for pattern recognition,
PR(34), No. 1, January 2001, pp. 139-150.
Elsevier DOI 0010
BibRef
And:
Local Reinforcement Learning for Object Recognition,
ICPR98(Vol I: 272-274).
IEEE DOI 9808
BibRef
Earlier:
Delayed Reinforcement Learning for Closed-Loop Object Recognition,
ICPR96(IV: 310-314).
IEEE DOI 9608
BibRef
And: ARPA96(1429-1436). (Univ. of California, Riverside, USA) BibRef

Pauli, J.[Josef],
Learning to Recognize and Grasp Objects,
MachLearn(31), No. 1-3, Apr-Jun 1998, pp. 239-258. 9809
BibRef
Earlier:
Learning Operators for View-Independent Object Recognition,
BMVC96(Poster Session 1). 9608
Christian-Albrechts-Universitat, Germany BibRef

Kervrann, C.[Charles],
Learning probabilistic deformation models from image sequences,
SP(71), No. 2, 15 December 1998, pp. 155-171. BibRef 9812

Newman, R.A.[Rhys A.],
A New Model of Computation for Learning Vision Modules from Examples,
JMIV(11), No. 1, September 1999, pp. 45-63.
DOI Link BibRef 9909

Newman, R.A.[Rhys A.],
Madura: A Language for Learning Vision Programs from Examples,
JMIV(11), No. 1, September 1999, pp. 65-90.
DOI Link BibRef 9909

Pittore, M.[Massimiliano], Campani, M.[Marco], Verri, A.[Alessandro],
Learning to Recognize Visual Dynamic Events from Examples,
IJCV(38), No. 1, June 2000, pp. 35-44.
DOI Link 0006
BibRef

Baldoni, M.[Matteo], Baroglio, C.[Cristina], Cavagnino, D.[Davide],
Use of IFS Codes for Learning 2D Isolated-Object Classification Systems,
CVIU(77), No. 3, March 2000, pp. 371-387.
DOI Link 0004
BibRef

Guo, C.E.[Cheng-En], Zhu, S.C.[Song-Chun], Wu, Y.N.[Ying Nian],
Modeling Visual Patterns by Integrating Descriptive and Generative Methods,
IJCV(53), No. 1, June 2003, pp. 5-29.
DOI Link 0304
BibRef
Earlier:
Visual Learning by Integrating Descriptive and Generative Methods,
ICCV01(I: 370-377).
IEEE DOI 0106
BibRef

Tian, Q.[Qi], Wu, Y.[Ying], Yu, J.[Jie], Huang, T.S.[Thomas S.],
Self-supervised learning based on discriminative nonlinear features for image classification,
PR(38), No. 6, June 2005, pp. 903-917.
Elsevier DOI 0501
BibRef

Wu, Y.[Ying], Huang, T.S.[Thomas S.], Toyama, K.[Kentaro],
Self-Supervised Learning for Object Recognition based on Kernel Discriminant-EM Algorithm,
ICCV01(I: 275-280).
IEEE DOI 0106
BibRef

Xu, Y., Duygulu, P., Saber, E., Tekalp, A.M., Yarman-Vural, F.T.,
Object-based image labeling through learning by example and multi-level segmentation,
PR(36), No. 6, June 2003, pp. 1407-1423.
Elsevier DOI 0304
See also Object Formation by Learning in Visual Databases using Hierarchical Content Description. BibRef

Xu, Y.W.[Yao-Wu], Saber, E.[Eli], Tekalp, A.M.[A. Murat],
Object segmentation and labeling by learning from examples,
IP(12), No. 6, June 2003, pp. 627-638.
IEEE DOI 0307
BibRef

Xu, Y.W.[Yao-Wu], Saber, E.[Eli], Tekalp, A.M.[A. Murat],
Dynamic learning from multiple examples for semantic object segmentation and search,
CVIU(95), No. 3, September 2004, pp. 334-353.
Elsevier DOI 0409
BibRef

Agarwal, S.[Shivani], Awan, A.[Aatif], and Roth, D.[Dan],
Learning to Detect Objects in Images via a Sparse, Part-Based Representation,
PAMI(26), No. 11, November 2004, pp. 1475-1490.
IEEE Abstract. Or:
PDF File.
WWW Link. 0410
Dataset, Vehicles. Detecting specific object classes (e.g. cars). BibRef

Agarwal, S., Roth, D.,
Learning a Sparse Representation for Object Detection,
ECCV02(IV: 113 ff.).
Springer DOI Or:
PDF File. 0205
BibRef

Ferencz, A.[Andras], Learned-Miller, E.G.[Erik G.], Malik, J.[Jitendra],
Learning to Locate Informative Features for Visual Identification,
IJCV(77), No. 1-3, May 2008, pp. 3-24.
Springer DOI 0803
BibRef
Earlier:
Building a Classification Cascade for Visual Identification from One Example,
ICCV05(I: 286-293).
IEEE DOI 0510
E.g. identify a particular car, given one example of the car. Predict most discrimitive features. BibRef

Weinman, J.J.[Jerod J.], Learned-Miller, E.G.[Erik G.],
Improving Recognition of Novel Input with Similarity,
CVPR06(I: 308-315).
IEEE DOI 0606
BibRef

Jain, V., Ferencz, A.[Andras], Learned-Miller, E.G.[Erik G.],
Discriminative Training of Hyper-feature Models for Object Identification,
BMVC06(I:357).
PDF File. 0609
BibRef

Huang, G.B.[Gary B.], Learned-Miller, E.G.[Erik G.],
Learning class-specific image transformations with higher-order Boltzmann machines,
SMiCV10(25-32).
IEEE DOI 1006
E.g. faces as the first example. BibRef

Vijayanarasimhan, S.[Sudheendra], Jain, P.[Prateek], Grauman, K.[Kristen],
Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning,
PAMI(36), No. 2, February 2014, pp. 276-288.
IEEE DOI 1402
BibRef
Earlier:
Far-sighted active learning on a budget for image and video recognition,
CVPR10(3035-3042).
IEEE DOI 1006
data analysis. Select most informative instance first. BibRef

Kovashka, A.[Adriana], Vijayanarasimhan, S.[Sudheendra], Grauman, K.[Kristen],
Actively selecting annotations among objects and attributes,
ICCV11(1403-1410).
IEEE DOI 1201
Select based on which will give the most information (from human labeling). BibRef

Liu, Y.Z.[Ya-Zhuo], Zayas-Castro, J.L.[José L.], Fabri, P.[Peter], Huang, S.[Shuai],
Learning high-dimensional networks with nonlinear interactions by a novel tree-embedded graphical model,
PRL(49), No. 1, 2014, pp. 207-213.
Elsevier DOI 1410
High-dimensional network learning with both linear and nonlinear interactions. BibRef

Li, D.B.[De-Bang], Zhang, J.G.[Jun-Ge], Huang, K.Q.[Kai-Qi],
Universal adversarial perturbations against object detection,
PR(110), 2021, pp. 107584.
Elsevier DOI 2011
Adversarial examples, Object detection, Universal adversarial perturbation BibRef

Zhou, C., Zhang, J., Liu, J., Zhang, C., Shi, G., Hu, J.,
Bayesian Transfer Learning for Object Detection in Optical Remote Sensing Images,
GeoRS(58), No. 11, November 2020, pp. 7705-7719.
IEEE DOI 2011
Training, Object detection, Bayes methods, Detectors, Random access memory, Optical imaging, Optical sensors, transfer learning BibRef


Hao, M.[Miao], Liu, Y.T.[Yi-Tao], Zhang, X.Y.[Xiang-Yu], Sun, J.[Jian],
LabelEnc: A New Intermediate Supervision Method for Object Detection,
ECCV20(XXV:529-545).
Springer DOI 2011
BibRef

Li, Y.D.[Yan-Dong], Huang, D.[Di], Qin, D.F.[Dan-Feng], Wang, L.Q.[Li-Qiang], Gong, B.Q.[Bo-Qing],
Improving Object Detection with Selective Self-supervised Self-training,
ECCV20(XXIX: 589-607).
Springer DOI 2010
BibRef

Cheng, M.[Miao], Su, J.P.[Jin-Peng], Li, L.Y.[Lu-Yi], Zhou, X.M.[Xiang-Ming],
A-DFPN: Adversarial Learning and Deformation Feature Pyramid Networks for Object Detection,
ICIVC20(11-18)
IEEE DOI 2009
Feature extraction, Object detection, Detectors, Convolution, Semantics, Strain, Deconvolution, Deformation feature pyramid, adversarial learning BibRef

Ma, L.[Lin], Lu, Z.D.[Zheng-Dong], Shang, L.F.[Li-Feng], Li, H.[Hang],
Multimodal Convolutional Neural Networks for Matching Image and Sentence,
ICCV15(2623-2631)
IEEE DOI 1602
match image and text BibRef

Mao, J.H.[Jun-Hua], Wei, X.[Xu], Yang, Y.[Yi], Wang, J.[Jiang], Huang, Z.H.[Zhi-Heng], Yuille, A.L.[Alan L.],
Learning Like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images,
ICCV15(2533-2541)
IEEE DOI 1602
Adaptation models. Learning novel concepts. BibRef

Weng, J.Y.[Ju-Yang], Luciw, M.[Matthew],
Online learning for attention, recognition, and tracking by a single developmental framework,
OLCV10(7-14).
IEEE DOI 1006
Self-Aware and Self-Effecting. Human learning model. BibRef

Kveton, B.[Branislav], Valko, M.[Michal],
Learning from a single labeled face and a stream of unlabeled data,
FG13(1-8)
IEEE DOI 1309
face recognition BibRef

Kveton, B.[Branislav], Philipose, M.[Matthai], Valko, M.[Michal], Huang, L.[Ling],
Online semi-supervised perception: Real-time learning without explicit feedback,
OLCV10(15-21).
IEEE DOI 1006
BibRef

Li, Y.N.[Yuan-Ning], Wang, W.Q.[Wei-Qiang], Gao, W.[Wen],
Object Recognition Based on Dependent Pachinko Allocation Model,
ICIP07(V: 337-340).
IEEE DOI 0709
Pachinko Allocation Model: Learning using graph structures. (From the learning community) BibRef

Wu, Y.[Yang], Yuan, Z.J.[Ze-Jian], Liu, Y.L.[Yuan-Liu], Zheng, N.N.[Nan-Ning],
Discriminative structured outputs prediction model and its efficient online learning algorithm,
Emergent09(2087-2094).
IEEE DOI 0910
Deal with the explosion of data and requirement for detailed analysis of scenes. Not just class, but structured labelling. BibRef

Wu, Y.[Yang], Zheng, N.N.[Nan-Ning], You, Q.B.[Qu-Bo], Du, S.Y.[Shao-Yi],
Object Recognition by Learning Informative, Biologically Inspired Visual Features,
ICIP07(I: 181-184).
IEEE DOI 0709
BibRef

Xu, W.J.[Wen-Jie], Wu, J.K.[Jian-Kang], Huang, Z.Y.[Zhi-Yong],
A maximum margin discriminative learning algorithm for temporal signals,
ICPR06(II: 460-463).
IEEE DOI 0609
BibRef

Forssen, P.E.[Per-Erik], Moe, A.[Anders],
Autonomous Learning of Object Appearances using Colour Contour Frames,
CRV06(3-3).
IEEE DOI 0607
Texture patch model. BibRef

Jojic, N.[Nebojsa], Winn, J.[John], Zitnick, L.[Larry],
Escaping local minima through hierarchical model selection: Automatic object discovery, segmentation, and tracking in video,
CVPR06(I: 117-124).
IEEE DOI 0606
In learning models the main structure comes out early, fine detail is later. BibRef

Cai, X.C.[Xiong-Cai], Sowmya, A.[Arcot], Trinder, J.[John],
Learning Parameter Tuning for Object Extraction,
ACCV06(I:868-877).
Springer DOI 0601
BibRef

Hadsell, R.[Raia], Chopra, S.[Sumit], Le Cun, Y.L.[Yann L.],
Dimensionality Reduction by Learning an Invariant Mapping,
CVPR06(II: 1735-1742).
IEEE DOI 0606
BibRef

Chopra, S.[Sumit], Hadsell, R.[Raia], Le Cun, Y.L.[Yann L.],
Learning a Similarity Metric Discriminatively, with Application to Face Verification,
CVPR05(I: 539-546).
IEEE DOI 0507
BibRef

Edelman, S.[Shimon], Intrator, N.[Nathan], Jacobson, J.S.[Judah S.],
Unsupervised Learning of Visual Structure,
BMCV02(629 ff.).
Springer DOI 0303
BibRef

Loos, H.S.[Hartmut S.], von der Malsburg, C.[Christoph],
1-Click Learning of Object Models for Recognition,
BMCV02(377 ff.).
Springer DOI 0303
BibRef

Lömker, F., Sagerer, G.,
A Multimodal System for Object Learning,
DAGM02(490 ff.).
Springer DOI 0303
BibRef

Lashkia, G.V.,
Learning with relevant features and examples,
ICPR02(II: 68-71).
IEEE DOI 0211
BibRef

Roobaert, D.[Danny], Zillich, M.[Michael], Eklundh, J.O.[Jan-Olof],
A Pure Learning Approach to Background-Invariant Object Recognition Using Pedagogical Support Vector Learning,
CVPR01(II:351-357).
IEEE DOI 0110
BibRef

Caelli, T.M.,
Learning Image Feature Extraction: Modeling, Tracking and Predicting Human Performance,
ICPR00(Vol II: 215-218).
IEEE DOI 0009
BibRef

Duta, N.[Nicolae], Jain, A.K.[Anil K.], Dubuisson-Jolly, M.P.[Marie-Pierre],
Learning-based Object Detection in Cardiac MR Images,
ICCV99(1210-1216).
IEEE DOI BibRef 9900

Duta, N.[Nicolae], Jain, A.K.[Anil K.], Dubuisson-Jolly, M.P.[Marie-Pierre],
Learning 2D Shape Models,
CVPR99(II: 8-14).
IEEE DOI Clustering training shapes. BibRef 9900

Kato, T., Ninomiya, Y.,
An Approach to Vehicle Recognition Using Supervised Learning,
MVA98(xx-yy). BibRef 9800

Vetter, T.[Thomas], Jones, M.J.[Michael J.], Poggio, T.[Tomaso],
A Bootstrapping Algorithm for Learning Linear Models of Object Classes,
CVPR97(40-46).
IEEE DOI 9704
BibRef
And: DARPA97(1373-1378). Faces and digits. BibRef

Shu, D.B.[David B.], and Li, C.C.,
Reordering of Surface Feature Vectors in Training for 3-D Object Recognition,
ICPR86(111-115). BibRef 8600

Tomita, F.,
A Learning Vision System for 2D Object Recognition,
IJCAI83(1132-1135). BibRef 8300

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Multiple Instance Learning .


Last update:Dec 3, 2020 at 16:16:50