Texture Segmentation for Remote Sensing Image Based on Texture-Topic Model

Hao Feng Zhiguo JiangBeijing University of Aeronautics & Astronautics

Xingmin HanBeijing University of Technology

IGARSS 2011

Image Processing Center

watersandgrasstree 1, high densitytree 2, middle densitytree 3, low density

Proposed Method

-Topic Model: Latent Dirichlet Allocation -LDA is a generative probabilistic model of a corpus. -LDA automatically clusters words into “topics” and documents into mixtures of topics. -Bag-of-Words Assumption - Connecting word and feature descriptor

-Texture is topic, pixel (feature descriptor) is word.

Previous Works

• Li Fei-Fei, Pietro Perona, CVPR 2005• Supervised LDA• Natural Scene Categorization

•Erik B. Sudderth, IJCV 2008• Transformed Dirichlet Process• Model natural scene with spatial constraint

•Marie Liénou,…, IEEE Geoscience and Remote Sensing Letter 2010 Dragos Bratasanu, Lon Nedelcu, Mihai Datcu, IGARSS 2011• Annotation of Satellite Images Using LDA

•Xian Sun,…, IEEE Geoscience and Remote Sensing Letter 2010 • Model geospatial object using LDA

Latent Dirichlet Allocation-LDA is a generative probabilistic model of a corpus. -Documents are represented as random mixtures over latent topics-where a topic is characterized by a distribution over words.

• Let’s assume that all the words within a document are exchangeable.

Latent Dirichlet Allocation

For each document,• Choose ~ Dirichlet()• For each of the N words :

– Choose a topic zn ~ Multinomial()– Choose a word from , a multinomial probability

conditioned on the topic zn.

),()()|(),|,,(1

nn

N

nn zpzppzp

n

n ),( nn zp

[blei 2003]

Uni

vers

ity

educ

ation

stud

ent

cour

se

unde

rgra

duac

e

post

grad

uate

……..

envi

ronm

ent

debt

labo

r

Latent Dirichlet Allocation

This will mean that the Open University, which provides degree courses by distance learning, will have among the lowest fees in England. Vice chancellor Martin Bean promised "high-quality, flexible and great value-for-money education for all". The majority of universities will charge £9,000 for some or all courses. More than two-thirds of the Open University's students are studying part-time - and the university will be expecting to benefit from the introduction of loans for part-time students. For a typical part-time Open University student, studying at the level of half of full-time, the fees will be £2,500 per year. Mr Bean said that the extension of the loan system represented the "beginning of a new era for part-time students". Younger students At present the university has 264,000 students taking more than 600 undergraduate and postgraduate courses and professional qualifications - ……. [BBC News]

Topic: Education

word

Frequency

……..

Dictionary

Latent Dirichlet Allocation

θ

zLatent topic

wBag-of-words

Building 1 Building 2

Topic 1 Topic 2 Topic 3

Topic Distribution

Spatial Constraint LDAThe William Randolph Hearst Foundation will give $1.25 million to Lincoln Center, Metropolitan Opera Co., New York Philharmonic and Juilliard School. “Our board felt that we had a real opportunity to make a mark on the future of the performing arts with these grants an act every bit as important as our traditional areas of support in health, medical research, education and the social services,” Hearst Foundation President Randolph A. Hearst said Monday in announcing the grants. Lincoln Center’s share will be $200,000 for its new building, which will house young artists and provide new public facilities. The Metropolitan Opera Co. and New York Philharmonic will receive $400,000 each. The Juilliard School, where music and the performing arts are taught, will get $250,000. The Hearst Foundation, a leading supporter of the Lincoln Center Consolidated Corporate Fund, will make its usual annual $100,000 donation, too.

2,600,000,000 results

448,000,000 results13,400,000 results

57,100 results

Spatial Constraint LDA

Neighbors

Gaussian Parameters

),,|(),,,,|,(),|()|()|(),,|,,(

nzznnzznnn

nnn

nnnnPzPzPzPP

HzrP

Dirichlet Distribution

Multinominal Distribution

Multinominal Distribution

Normal Inverse Wishart Gaussian Distribution

Spatial Constraint LDA

1) For each image, Choose ~Dirichlet().

2) For each pixel, draw texture-topic zn ~ Multinominal() .

3) For a topic zn, choose Gaussian parameters

4) Choose the visual word

5) Given the selected texture-topic zn and word , choose word

)(~),( HWishartnn zz

),(min~ nn zalMultino

n ),,(~ nzzn nnGaussian

Spatial Constraint LDA

Red: Considered Word (feature Descriptor)

Neighboring words

z

Example:

r

w

Word

Experiments1) Textures Segment

Brodatz texture and texture combination

4 dimension Haar feature

500 words visual dictionary

2) Remote Sensing Images

200 dimension DAISY descriptor

1000 words visual dictionary

Results

Texture model

Texture image Texture imageVisual word map Visual word map

Results

Region 1 2 3 4 5Recall 0.94 0.92 0.91 0.89 0.99False positive 0.06 0.02 0.01 0.02 0.09

1

2

3

4 5

Region 1 2 3 4 5Recall 0.95 0.90 0.91 0.90 0.88False positive 0.15 0.01 0.02 0.03 0.06

1

2

3

4 5

Results

Tree 1Tree 2

GrasslandTree 3

Road/Sand/Land

GarssRoadTreeRooftopPark

Conclusion

-Model Texture using LDA-Introduce Neighborhood constraint to LDA-Segment texture combinations and remote sensing images

-Noise in sampling results-Bag-of-words-Speed-Feature descriptor-More information….

Thank you

fenghao@sa.buaa.edu.cn