7.1.7.2 Generic Object Detection

Chapter Contents (Back)
Generic Objects. See also Region of Interest Detection, ROI.

Mikolajczyk, K.[Krystian], Tuytelaars, T.[Tinne], Schmid, C., Zisserman, A., Matas, J.G., Schaffalitzky, F., Kadir, T., Van Gool, L.J.,
A Comparison of Affine Region Detectors,
IJCV(65), No. 1-2, November 2005, pp. 43-72.
Springer DOI 0604
Six types of detectors are included: detectors based on affine normalization around Harris ( See also Scale and Affine Invariant Interest Point Detectors. ); ( See also Automated location matching in movies. ) and Hessian points ( See also Scale and Affine Invariant Interest Point Detectors. ), MSER: Maximally stable extremal regions ( See also Robust Wide Baseline Stereo from Maximally Stable Extremal Regions. ); an edge-based region detector (Tuytelaars and Van Gool, 1999) and Intensity extrema ( See also Matching Widely Separated Views Based on Affine Invariant Regions. ), Salient regions ( See also Affine Invariant Salient Region Detector, An. ). BibRef

Seo, H.J.[Hae Jong], Milanfar, P.[Peyman],
Training-Free, Generic Object Detection Using Locally Adaptive Regression Kernels,
PAMI(32), No. 9, September 2010, pp. 1688-1704.
IEEE DOI 1008
BibRef
Earlier:
Using local regression kernels for statistical object detection,
ICIP08(2380-2383).
IEEE DOI 0810
Detection-locaization to search without training. Single example of the object of interest to find similar matches. Local regression kernels. BibRef

Biswas, S.K.[Sujoy Kumar], Milanfar, P.[Peyman],
One Shot Detection with Laplacian Object and Fast Matrix Cosine Similarity,
PAMI(38), No. 3, March 2016, pp. 546-562.
IEEE DOI 1602
BibRef
Earlier:
Laplacian object: One-shot object detection by locality preserving projection,
ICIP14(4062-4066)
IEEE DOI 1502
Covariance matrices. Search for single query in larger target image. BibRef

Alexe, B.[Bogdan], Deselaers, T.[Thomas], Ferrari, V.[Vittorio],
Measuring the Objectness of Image Windows,
PAMI(34), No. 11, November 2012, pp. 2189-2202.
IEEE DOI 1209
BibRef
Earlier:
What is an object?,
CVPR10(73-80).
IEEE DOI 1006
BibRef
And:
ClassCut for Unsupervised Class Segmentation,
ECCV10(V: 380-393).
Springer DOI 1009
BibRef
And: A2, A1, A3:
Localizing Objects While Learning Their Appearance,
ECCV10(IV: 452-466).
Springer DOI 1009
Does an image window contain an object (any object). A generic object measure. Objects with well defined boundaries vs. amorphous background elements. BibRef

Deselaers, T.[Thomas], Ferrari, V.[Vittorio],
Visual and semantic similarity in ImageNet,
CVPR11(1777-1784).
IEEE DOI 1106
BibRef
Earlier:
Global and efficient self-similarity for object classification and detection,
CVPR10(1633-1640).
IEEE DOI Video of talk:
WWW Link. 1006
BibRef

Deselaers, T.[Thomas], Alexe, B.[Bogdan], Ferrari, V.[Vittorio],
Weakly Supervised Localization and Learning with Generic Knowledge,
IJCV(100), No. 3, December 2012, pp. 275-293.
WWW Link. 1210
BibRef

Pan, H.[Hong], Zhu, Y.P.[Ya-Ping], Xia, L.Z.[Liang-Zheng],
Efficient and accurate face detection using heterogeneous feature descriptors and feature selection,
CVIU(117), No. 1, January 2013, pp. 12-28.
Elsevier DOI 1212
BibRef
Earlier:
Fusing multi-feature representation and PSO-Adaboost based feature selection for reliable frontal face detection,
ICIP13(2998-3002)
IEEE DOI 1402
Cascade classifiers Face detection; PSO; Adaboost; Feature selection; Cascade classifier BibRef

Pan, H.[Hong], Zhu, Y.P.[Ya-Ping], Qin, A.K., Xia, L.Z.[Liang-Zheng],
Mining heterogeneous class-specific codebook for categorical object detection and classification,
ICIP13(3132-3136)
IEEE DOI 1402
Class-specific codebook BibRef

Pan, H.[Hong], Olsen, S.I.[S°ren Ingvor], Zhu, Y.P.[Ya-Ping],
Feature representation of RGB-D images using joint spatial-depth feature pooling,
PRL(80), No. 1, 2016, pp. 239-248.
Elsevier DOI 1609
BibRef
Earlier:
Object classification from RGB-D images using depth context kernel descriptors,
ICIP15(512-516)
IEEE DOI 1512
BibRef
Earlier:
Joint Spatial-Depth Feature Pooling for RGB-D Object Classification,
SCIA15(314-326).
Springer DOI 1506
BibRef
Earlier:
Object Classification and Detection with Context Kernel Descriptors,
CIARP14(827-835).
Springer DOI 1411
RGB-D feature representation. Context cue BibRef

Pan, H.[Hong], Zhu, Y.P.[Ya-Ping], Xia, S.[Siyu], Qin, K.[Kai],
Improved generic categorical object detection fusing depth cue with 2D appearance and shape features,
ICPR12(1467-1470).
WWW Link. 1302
BibRef

Pan, H.[Hong], Xia, L.Z.[Liang-Zheng], Nguyen, T.Q.[Truong Q.],
Robust object detection scheme using feature selection,
ICIP10(849-852).
IEEE DOI 1009
BibRef

Torrent, A.[Albert], Lladˇ, X.[Xavier], Freixenet, J.[Jordi], Torralba, A.[Antonio],
A boosting approach for the simultaneous detection and segmentation of generic objects,
PRL(34), No. 13, 2013, pp. 1490-1498.
Elsevier DOI 1307
BibRef
Earlier:
Simultaneous detection and segmentation for generic objects,
ICIP11(653-656).
IEEE DOI 1201
Object detection. General framework, not just one kind of object. BibRef

Torrent, A.[Albert], Llado, X.[Xavier], Freixenet, J.[Jordi],
Semiautomatic labeling of generic objects for enlarging annotated image databases,
ICIP12(2889-2892).
IEEE DOI 1302
BibRef

Wang, X.[Xiaoyu], Yang, M.[Ming], Zhu, S.H.[Sheng-Huo], Lin, Y.Q.[Yuan-Qing],
Regionlets for Generic Object Detection,
PAMI(37), No. 10, October 2015, pp. 2071-2084.
IEEE DOI 1509
BibRef
Earlier: ICCV13(17-24)
IEEE DOI 1403
Boosting. DPM BibRef


Lu, S.[Song], Mahadevan, V.[Vijay], Vasconcelos, N.[Nuno],
Learning Optimal Seeds for Diffusion-Based Salient Object Detection,
CVPR14(2790-2797)
IEEE DOI 1409
diffusion;salient object;seed BibRef

Jiang, P., Vasconcelos, N., Peng, J.,
Generic Promotion of Diffusion-Based Salient Object Detection,
ICCV15(217-225)
IEEE DOI 1602
Algorithm design and analysis BibRef

Novotny, D.[David], Matas, J.G.[Jiri G.],
Cascaded Sparse Spatial Bins for Efficient and Effective Generic Object Detection,
ICCV15(1152-1160)
IEEE DOI 1602
Detectors based on HoG. BibRef

Zou, W.[Will], Wang, X.Y.[Xiao-Yu], Sun, M.[Miao], Lin, Y.Q.[Yuan-Qing],
Generic Object Detection with Dense Neural Patterns and Regionlets,
BMVC14(xx-yy).
HTML Version. 1410
BibRef

Siva, P.[Parthipan], Russell, C.[Chris], Xiang, T.[Tao], Agapito, L.[Lourdes],
Looking Beyond the Image: Unsupervised Learning for Object Saliency and Detection,
CVPR13(3238-3245)
IEEE DOI 1309
Generic Object Detection; Object Saliency; Weakly Supervised Learning BibRef

Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Interest Operators, Interest Points, Feature Points, Salient Points .


Last update:Nov 18, 2017 at 20:56:18