Horowitz, S.L., and
Pavlidis, T.,
Picture Segmentation by a Tree Traversal Algorithm,
JACM(23), No. 2, April 1976, pp. 368-388.
Segmentation, Split and Merge. The standard split and merge basic reference. The basics
are, split the image into regular shapes (quarters), if it is non-uniform
recursively split the subimages.
When no more splits, merge adjacent similar subimages, and
merge those remaining that are too small.
BibRef
7604
Horowitz, S.L., and
Pavlidis, T.,
Picture Segmentation by a Directed Split and Merge Procedure,
ICPR74(424-433).
BibRef
7400
CMetImAly77(101-11).
Good early reference to the method.
BibRef
Horowitz, S.L., and
Pavlidis, T.,
A Graph-Theoretic Approach to Picture Processing,
CGIP(7), No. 2, April 1978, pp. 282-291.
WWW Version.
BibRef
7804
Earlier:
Picture Processing by Graph Analysis,
CGPR75(125-129).
BibRef
Tanimoto, S.L., and
Pavlidis, T.,
The Editing of Picture Segmentations Using Local Analysis of Graphs,
CACM(20), No. 4, April 1977, pp. 223-229.
BibRef
7704
Pavlidis, T.,
Tanimoto, S.L.,
Texture Identification by a Directed Split-and Merge Procedure,
CGPR75(201-203).
BibRef
7500
Gupta, J.N., and
Wintz, P.A.,
A Boundary Finding Algorithm and Its Applications,
CirSys(22), No. 4, April 1975, pp. 351-362.
Segmentation, Texture. Merging on texture, use one or both of first or second order statistics.
BibRef
7504
Lemkin, P.,
An Approach to Region Splitting,
CGIP(10), No. 3, June 1979, pp. 281-288.
BibRef
7906
Browning, J.D., and
Tanimoto, S.L.,
Segmentation of Pictures into Regions with a Tile-by-Tile Method,
PR(15), No. 1, 1982, pp. 1-10.
WWW Version. Extension of split and merge to handle large images by looking at a small
portion at one time. Grouping must allow for boundary conditions.
BibRef
8200
Browning, J.D.,
A Method for Picture Segmentation by Parts:
Split and Group with Linking,
PRAI-78(191).
BibRef
7800
Chen, M.H., and
Pavlidis, T.,
Image Seaming for Segmentation on Parallel Architecture,
PAMI(12), No. 6, June 1990, pp. 588-594.
IEEE Abstract. IEEE Top Reference.
WWW Version. Same problems as the above paper, but with architecture issues.
BibRef
9006
Pavlidis, T., and
Liow, Y.T.,
Integrating Region Growing and Edge Detection,
PAMI(12), No. 3, March 1990, pp. 225-233.
IEEE Abstract. IEEE Top Reference.
WWW Version.
BibRef
9003
Earlier:
CVPR88(208-214).
IEEE Abstract. IEEE Top Reference.
Segmentation, Edges.
Use edges in the merging portion of a split and merge
segmentation algorithm.
BibRef
Autonisse, H.J.,
Image Segmentation in Pyramids,
CGIP(19), No. 4, 1982, pp. 367-383.
BibRef
8200
Spann, M.,
Wilson, R.,
A Quad-Tree Approach to Image Segmentation Which Combines Statistical
and Spatial Information,
PR(18), No. 3-4, 1985, pp. 257-269.
WWW Version.
BibRef
8500
Cheevasuvit, F.,
Maitre, H., and
Vidal-Madjar, D.,
A Robust Method for Picture Segmentation Based on a
Split-and-Merge Procedure,
CVGIP(34), No. 3, June 1986, pp. 268-281.
The aim is to get the consistent regions, the method is to segment
all members of the sequence, reduce regions to an ellipse, and
keep those regions whose ellipses are consistent.
BibRef
8606
Laprade, R.H.,
Split-and-Merge Segmentation of Aerial Photographs,
CVGIP(44), No. 1, October 1988, pp. 77-86.
Lockheed work on segmentation. This uses a facet type
representation of the resulting regions and the parameters
are also used in the merge phase.
BibRef
8810
Doherty, M.F.,
Bjorklund, C.M., and
Noga, M.T.,
Split-Merge-merge: An Enhanced Segmentation Capability,
CVPR86(325-330).
Add another merge step with more hueristics to eliminate the
standard small region problems.
BibRef
8600
Lee, C.H.[Chin-Hwa],
Recursive Region Splitting at Hierarchial Scope Views,
CVGIP(33), No. 2, February 1986, pp. 237-258.
WWW Version.
BibRef
8602
And:
Image Surface Approximation with Irregular Samples,
PAMI(11), No. 2, February 1989, pp. 206-212.
IEEE Abstract. IEEE Top Reference.
WWW Version.
Segmentation, Multi-Level. This method combines the regular splitting, and the quad-tree data
structure of the split and merge techniques with the general
threshold based region extraction method of the recursive splitting
techniques. The main problem being addressed is how to merge the
regions generated in one branch of the quad-tree with those in
spatially adjacent branches of the tree. This requires an analysis
of regions that touch the boundaries of the quad-tree nodes to
determine how they should extend or connect to regions in the other nodes.
BibRef
Wu, X.,
Adaptive Split-and-Merge Segmentation Based on Piecewise
Least-Square Approximation,
PAMI(15), No. 8, August 1993, pp. 808-815.
IEEE Abstract. IEEE Top Reference.
WWW Version.
BibRef
9308
Doherty, M.F.,
Noga, M.T., and
Bjorklund, C.M.,
Use of Compound Predicates in Split-and-Merge Segmentation,
Lockheed Palo Alto Research Labs,
CVPR85(659-661).
Add constant texture measures to the standard splitting criteria.
BibRef
8500
Cohen, Jr., E.A.[Edgar A.],
Generalized Sloped Facet Models Useful in
Multispectral Image Analysis,
CVGIP(32), No. 2, November 1985, pp. 171-190.
Segmentation, Facet Model. Seems to combine split and merge techniques with the
facet model for analysis.
BibRef
8511
Jarvis, R.A.,
Computer Image Segmentation: First Partitions Using
Shared Near Neighbor Clustering,
TC(20), No. 9, September 1971, pp. 1025-1034.
BibRef
7109
And:
Purdue-TR-77-43, November 1977.
BibRef
And:
Computer Image Segmentation: Structured Merge Strategies,
Purdue-TR-77-44, November 1977.
BibRef
And: (Similar title)
Purdue-TR-75-45.
Color. Bottom-up - image fragment conglomeration. Uses a variety of
features and criteria to decide the merging of adjacent regions.
Border count is one of them. Hard to predict the results
analytically. Hypothesis concerning "low level" visual cohesion in
intensity and color - excluding texture. (I.e., the region growing
initialization step). (higher levels in TREE-75-44 and submitted
for publication); neighborhood size, threshold of similarity
rating; region grower initialization still same problem of using
8x8 elements as smallest element (no times given).
(TC(20) is 1971, or is it TC(22)?)
BibRef
Jarvis, R.A.,
Image Segmentation by Interactively Combining Line, Region, and Semantic
Structure,
CGPR75(279-288).
BibRef
7500
Jarvis, R.A.,
Shared Near Neighbor Maximal Spanning Trees for Cluster Analysis,
unknown location (UMd report?) Basic method: iteratively add the
minimum link which adds a node to the tree. Add the least weight
except when it causes a loop.
Clustering: no unique optimal solution, any method gives
different results on various non-linear transformations of
measurement space.
7700
BibRef
Khan, G.N.,
Gillies, D.F.,
Parallel-Hierarchical Image Partitioning and Region Extraction,
CVIP92(123-140).
BibRef
9200
Wu, Z., and
Leahy, R.,
An Optimal Graph Theoretic Approach to Data Clustering:
Theory and Its Application to Image Segmentation,
PAMI(15), No. 11, November 1993, pp. 1101-1113.
IEEE Abstract. IEEE Top Reference.
WWW Version.
BibRef
9311
Wu, Z.,
Leahy, R.,
Image segmentation via edge contour finding: a graph theoretic approach,
CVPR92(613-619).
IEEE Abstract. IEEE Top Reference.
0403
BibRef
Panjwani, D.K.[Dileep K.],
Healey, G.,
Markov Random-Field Models for Unsupervised Segmentation
of Textured Color Images,
PAMI(17), No. 10, October 1995, pp. 939-954.
IEEE Abstract. IEEE Top Reference.
WWW Version.
Markov Random Field. Abstract:
HTML Version.
BibRef
9510
And:
Erratta for Rotated Figures,
PAMI(17), No. 11, November 1995, pp. 1128-1128.
IEEE Top Reference.
BibRef
Earlier:
Results Using Random Field Models for the Segmentation of Color Images,
ICCV95(714-719).
WWW Version.
WWW Version.
Segmentation, MRF.
Color. Segmentation using split and merge type of algorithm.
See also Analytical and Experimental Study of the Performance of Markov Random-Fields Applied to Textured Images Using Small Samples, An.
BibRef
Panjwani, D.K., and
Healey, G.,
Unsupervised Segmentation of Textured Color Images Using Markov
Random Field Models,
CVPR93(776-777).
IEEE Abstract. IEEE Top Reference.
BibRef
9300
Panjwani, D.K., and
Healey, G.,
Selecting Neighbors in Random Field Models for Color Images,
ICIP94(II: 56-60).
WWW Version.
9411Abstract:
HTML Version.
BibRef
Fiorio, C., and
Gustedt, J.,
Two Linear Time Union-Find Strategies for Image Processing,
TCS(A: 154), No. 2, 1996, pp. 165-181.
ON2 algorithm.
BibRef
9600
Xu, Y.,
Uberbacher, E.C.,
2D Image Segmentation Using Minimum Spanning-Trees,
IVC(15), No. 1, January 1997, pp. 47-57.
WWW Version.
9702
BibRef
Li, C.T.,
Multiresolution image segmentation integrating Gibbs sampler and region
merging algorithm,
SP(83), No. 1, January 2003, pp. 67-78.
HTML Version.
0211
BibRef
Li, C.T.[Chang-Tsun],
Chiao, R.[Randy],
Multiresolution genetic clustering algorithm for texture segmentation,
IVC(21), No. 11, October 2003, pp. 955-966.
WWW Version.
0310
BibRef
Li, C.T.[Chang-Tsun],
Chiao, R.,
Unsupervised texture segmentation using multiresolution hybrid genetic
algorithm,
ICIP03(II: 1033-1036).
IEEE Abstract. IEEE Top Reference.
0312
BibRef
Storkey, A.J.[Amos J.],
Williams, C.K.I.[Christopher K.I.],
Image modeling with position-encoding dynamic trees,
PAMI(25), No. 7, July 2003, pp. 859-871.
IEEE Abstract. IEEE Top Reference.
0307Tree descriptions for segmentation.
BibRef
Adams, N.J.,
Storkey, A.J.,
Ghahramani, Z.,
Williams, C.K.I.,
MFDTs: Mean Field Dynamic Trees,
ICPR00(Vol III: 147-150).
WWW Version.
HTML Version.
0009
BibRef
Chung, K.L.[Kuo-Liang],
Huang, H.L.[Hsu-Lien],
Lu, H.I.[Hsueh-I],
Efficient region segmentation on compressed gray images using quadtree
and shading representation,
PR(37), No. 8, August 2004, pp. 1591-1605.
WWW Version.
0407Segment the compressed image, in split-merge tree framework.
Compares to
See also Two Linear Time Union-Find Strategies for Image Processing.
BibRef
Chung, R.H.Y.[Ronald H.Y.],
Yung, N.H.C.[Nelson H.C.],
Cheung, P.Y.S.[Paul Y.S.],
An Efficient Parameterless Quadrilateral-Based Image Segmentation
Method,
PAMI(27), No. 9, September 2005, pp. 1446-1458.
WWW Version.
0508
BibRef
Grady, L.[Leo],
Schwartz, E.L.[Eric L.],
Isoperimetric Graph Partitioning for Image Segmentation,
PAMI(28), No. 3, March 2006, pp. 469-475.
WWW Version.
0602
BibRef
Earlier:
Faster graph-theoretic image processing via small-world and quadtree
topologies,
CVPR04(II: 360-365).
IEEE Abstract. IEEE Top Reference.
0408Segmentation approach.
See also Random Walks for Image Segmentation.
BibRef
Kumar, S.,
Ong, S.H.,
Ranganath, S.,
Ong, T.C.,
Chew, F.T.,
A rule-based approach for robust clump splitting,
PR(39), No. 6, June 2006, pp. 1088-1098.
WWW Version.
0604Concavity analysis; Overlapping objects; Clump splitting
BibRef
Pichel, J.C.[Juan C.],
Singh, D.E.[David E.],
Rivera, F.F.[Francisco F.],
Image segmentation based on merging of sub-optimal segmentations,
PRL(27), No. 10, 15 July 2006, pp. 1105-1116.
WWW Version.
0606Region-merging heuristic; Segmentation evaluation
BibRef
Duarte, A.[Abraham],
Sánchez, Á.[Ángel],
Fernández, F.[Felipe],
Montemayor, A.S.[Antonio S.],
Improving image segmentation quality through effective region merging
using a hierarchical social metaheuristic,
PRL(27), No. 11, August 2006, pp. 1239-1251.
WWW Version. Evolutionary metaheuristics; Watershed; Region merging;
Graph-based segmentation; Hierarchical social algorithm
0606
BibRef
Ouzounis, G.K.[Georgios K.],
Wilkinson, M.H.F.[Michael H.F.],
Mask-Based Second-Generation Connectivity and Attribute Filters,
PAMI(29), No. 6, June 2007, pp. 990-1004.
WWW Version.
0704
BibRef
Earlier:
Countering Oversegmentation in Partitioning-Based Connectivities,
ICIP05(III: 844-847).
WWW Version.
0512
BibRef
Wu, Y.T.[Yi-Ta],
Shih, F.Y.[Frank Y.],
Shi, J.Z.[Jia-Zheng],
Wu, Y.T.[Yih-Tyng],
A top-down region dividing approach for image segmentation,
PR(41), No. 6, June 2008, pp. 1948-1960.
WWW Version.
0802Image segmentation; Feature-based segmentation; Spatial-based segmentation;
Watershed; Medical image analysis
BibRef
Blanton, W.B.[W. Brendan],
Barner, K.E.[Kenneth E.],
Texture-Based Infrared Image Segmentation by Combined Merging and
Partitioning,
ICIP07(II: 45-48).
WWW Version.
0709
BibRef
Tehami, S.[Samy],
Bigand, A.[André],
Colot, O.[Olivier],
Color Image Segmentation Based on Type-2 Fuzzy Sets and Region Merging,
ACIVS07(943-954).
WWW Version.
0708
BibRef
Micusik, B.[Banislav],
Pajdla, T.[Tomas],
Multi-label image segmentation via max-sum solver,
CVPR07(1-6).
WWW Version.
0706
BibRef
Radhakrishnan, M.L.,
Su, S.L.,
Dead-End Elimination as a Heuristic for Min-Cut Image Segmentation,
ICIP06(2429-2432).
0610
WWW Version.
BibRef
Zhan, Y.W.[Yao-Wen],
Wang, W.Q.[Wei-Qiang],
Gao, W.[Wen],
A Robust Split-and-Merge Text Segmentation Approach for Images,
ICPR06(II: 1002-1005).
WWW Version.
0609
BibRef
Varma, M.[Manik],
Ray, D.[Debajyoti],
Learning The Discriminative Power-Invariance Trade-Off,
ICCV07(1-8).
WWW Version.
0710
BibRef
He, X.M.[Xu-Ming],
Zemel, R.S.[Richard S.],
Ray, D.[Debajyoti],
Learning and Incorporating Top-Down Cues in Image Segmentation,
ECCV06(I: 338-351).
WWW Version.
0608
BibRef
Levin, A.[Anat],
Weiss, Y.[Yair],
Learning to Combine Bottom-Up and Top-Down Segmentation,
ECCV06(IV: 581-594).
WWW Version.
0608
BibRef
Estrada, F.J.,
Jepson, A.D.,
Chennubhotla, C.,
Spectral Embedding and Min Cut for Image Segmentation,
BMVC04(xx-yy).
HTML Version.
0508
BibRef
Tolliver, D.A.[David A.],
Collins, R.T.[Robert T.],
Baker, S.[Simon],
Multilevel Spectral Partitioning for Efficient Image Segmentation and
Tracking,
WACV05(I: 414-420).
WWW Version.
0502Normalized cut image segmentation.
BibRef
Merigot, A.,
Revisiting image splitting,
CIAP03(314-319).
IEEE Abstract. IEEE Top Reference.
0310
BibRef
Kaplan, L.M.,
Oh, S.M.,
Yoon, Y.S.,
McClellan, J.H.,
Target Detection Features for Pruned Quadtree Image Formation,
CVBVS01(xx-yy).
0110
BibRef
Borges, G.A.,
Aldon, M.J.,
A Split-and-merge Segmentation Algorithm for Line Extraction in 2-d
Range Images,
ICPR00(Vol I: 441-444).
WWW Version.
HTML Version.
0009
BibRef
Yang, H.S.[Hee Soo],
Lee, S.U.[Sang Uk],
Split-and-merge segmentation employing thresholding technique,
ICIP97(I: 239-242).
WWW Version.
9710
BibRef
Basman, A.[Antranig],
Lasenby, J.[Joan],
Cipolla, R.[Roberto],
Efficient region segmentation through 'creep-and-merge',
CIAP97(I: 223-230).
WWW Version.
9709
BibRef
Gevers, T.,
Smeulders, A.W.M.,
Combining Region Splitting and Edge Detection Through
Guided Delaunay Image Subdivision,
CVPR97(1021-1026).
IEEE Abstract. IEEE Top Reference.
WWW Version.
9704
BibRef
Kropatsch, W.G.,
Ben-Yacoub, S.,
A Revision of Pyramid Segmentation,
ICPR96(II: 477-481).
WWW Version.
9608(Technical Univ. Vienna, A)
BibRef
Kropatsch, W.G.,
Ben-Yacoub, S.,
A Universal Pyramid Segmentation Algorithm,
SPIE(2826), August 1996, pp. 216-224.
BibRef
9608
Gevers, T.,
Kajcovski, V.K.,
Image Segmentation by Directed Region Subdivision,
ICPR94(A:342-346).
WWW Version.
BibRef
9400
Schutte, K.,
Region Growing with Planar Facets,
SCIA93(719-725).
BibRef
9300
Kalvin, A.,
Peleg, S.,
Hummel, R.,
Pyramid Segmentation in 2D and 3D Images Using Local Optimization,
ICPR88(I: 276-278).
WWW Version.
8811
BibRef
Parvin, B.A.,
A Split and Merge Algorithm for Segmentation of Natural Scenes,
ICPR84(294-296).
BibRef
8400
Gerbrands, J.J.,
Backer, E.,
Split-and-Merge Segmentation of SLAR Imagery: Segmentation Consistency,
ICPR84(284-286).
BibRef
8400
Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Multi-level Segmentation and Smoothing Methods .