University at BuffaloThe State University of New York Keyblock Approach: Metadata Generation and Retrieval of Geographic Imagery Aidong Zhang Associate

University at Buffalo The State University of New York

Keyblock Approach: Metadata Generation and Retrieval of

Geographic Imagery

Aidong ZhangAssociate Professor

Director, Multimedia and Database LaboratoryComputer Science and Engineering

University at Buffalo


07.25. 2001


IntroductionObservations:

USGS, NIMA and NASA provide the archiving of large repositories of remote-sensing data.

New Issues: problem of resource selection. Given a query, where should a user start a search?

Our Approach:Design a metaserver on top of various visual

databases. Given a query, the metaserver first produces a

ranking of the databse sites and then distributes the queries to the selected databases.


Distributed System Architecture

GIS Database

GIS Database

GIS Database

GIS Database

Client Browser

Client Browser

Client Browser

Client Browser

Metasearch Agent

Meta Database

Metaserver

Query Manager

Metaserver (Our focus)

GIS database at remote sites

Client applications for visual display


METASEVERMETASEVER/DB/DB

Local Severs/DB

Local Severs/DBRanked DB List 1.GIS-SANF Server/DB

2.GIS-1999 Server/DB……7.GIS-FLOR Server/DB

Step 1

Step 2

GIS1998Server/DB

GIS1999Server/DB

GIS2000Server/DB

GISWNYServer/DB

GIS-SANFServer/DB

GIS-FLORServer/DB

GIS-FLOR2Server/DB

Matching Images

Users


Global View of Data Sources

METADATABASE

Color Texture ShapeFeature Classes

Templates

Database SitesDB1 DB2 DBn


Generating Templates

Images are clustered and the centroids of the clusters are chosen as templates.

Environment

Residential Water Grass Agriculture


Templates of local databases are collected in the metadatabase to represent the content of the databases

Statistical data:We can measure the similarity of images in the databases to

the templates. Using these similarity measurements, statistical data are

computed that capture the likelihood of a database containing data that are relevant to a template.

The relevant databases for a given query can be selected by determining the similarity of the query with metadatabase templates and ranking the database sites based on the visual relationships recorded between the databases and templates.

Metadatabase


Content-based Image Retrieval (CBIR)

Allow retrievals performed on various of image contents such as color, texture, shape, etc.

Visual queries are submitted to image database to find similar images

Feature extraction is the basis of CBIRFamous systems include QBIC,

VisualSeek, PhotoBook, etc.


Evaluation Measures

Effectiveness of CBIR

|__|

|____|

retrievedofset

relevantofsetretrievedofsetprecision

|__|

|____|

relevantofset

relevantofsetretrievedofsetrecall

set_of_retrieved images

set_of_relevantimages


Multi-scale Feature Representation

Multi-resolution wavelet representation of image:

Original image Scale 1 Scale 2 Scale 3



Keyblock Approach

Generalizing text retrieval techniques to image retrievalText IR: use keywords to index and retrieve

What are the “keywords” of an image?Region segments of imagesFeatures of imagesObjects of images

How to generate “keywords” of images?Keyblocks: select centroids of clusters


ImageDatabase Sampling

TrainingBlocks

Feature-based Clustering

(GLA,PNNA,etc.)

Codebook

Query ImageImage Encoding

Keyblock Generation

Content-based Image Retrieval

Feature Representation: BM, VM, HM, etc.

Query and Retrieval

Training Set


Keyblock Generation Various clustering algorithms can

be used. On original space partition/segment the images into smaller blocks,

and then select a subset of representative blocks.On feature space extract low-level feature vectors, such as color,

texture, and shape, from image segments/blocks, and then select a subset of representative feature vectors.


Step 1: Initialization Step 2: Clustering/Partition

Step 3: Recalculating Centroid Step 4: Substituting Centroid and Reiterating

Unsupervised Keyblock Selection


Training Images

Water Codebook

Keyblock Generation

Training Images

Keyblock Generation

Forest Codebook

(Water) (Forest)

Stage I

Merge CodebooksStage II

Stage III

LVQ-basedFine Tuning

Knowledge-based Keyblock Generation


Image Encoding

For each image in the database, decompose it into blocks.

Then, for each block, find the closest entry in the codebook and store the index correspondingly.

Now each image is a matrix of indices, which can be regarded as 1-dimensional in scan order. This property is very similar to a text document which is considered as a linear list of keywords in text-based IR.


…...

Codebook ( a list of keyblocks)

Original Image

Segmentation

Table Lookup

Segmented Image

Encoded Image

0 1 2 3 4 5 6 7 8 9 10

11 12 13 14 15 16 17 18 19 20 21

Block Encoding

Image Decoding

Reconstructed Image

1618 16 16

15 15 18 18

1919 19 16

1616 18 18


A raw image and the reconstructed images with different codebooks


Image Feature Representation and Retrieval

Main components: the list of encoded images. list of keyblocks. the CBIR model

f is the feature extraction mapping which generates the feature vector for each image ;

s is the similarity measure between feature vectors. It is used to generate the ranking in the retrieval stage.

the set of visual queries.

Mj IIID ,,,, 1

Ni cccC ,,,, 1),( sf

Q


Single-block ModelsBoolean Model and Vector Model are

widely used in IRadopt keywords to index and retrieve documents;assume that both documents in the database and

queries can be described by a set of mutually independent keywords.

Similar image feature representation models can be designed use keyblocks instead of keywords for images;individual keyblock's appearance in images is

important information.


Boolean ModelBM considers whether or not a keyblock appears. Wij = 1 if fij >= T, 0 otherwise. fij is the frequency of keyblock ci appearing in image Ij , T

is a threshold. The feature vectors of Ij and q can be considered as

strings of length N where i-th bit indicates whether or not ci appears.

SBM (q,dj ) = n11 * w11 + n00 * w00 n11 is the number of bits at which both Ij and q are 1n00 is the number of bits at which both Ij and q are 0w11 and w00 are the weights assigned to n11 and n00 , respectively.


Vector Model normalized frequencyinverse image frequency

idfi = log( M / Mi ) ,for ci

keyblock weights: wij = f*ij * idfi Similarity measure is the inner

product of Ij and q

)()(

)(

***

*),(

qq

q

WiNi WiWijN

i Wij

WiNi Wij

IjqSvm

11

1

ljNl

ijij

ff

f

1max

*


Histogram Model HM can be regarded as a special case of

VM where wij = fij.The feature vectors Ij and q are the

keyblock histograms.Similarity measure

where

),(11

),(djqdis

dqS jhm

N

i qWiWij

qWiWijabsdjqdis

1 1 )(

))((),(


N-block Models

The single-block models only focus on individual keyblock’s appearance, the correlation among keyblocks are not counted in.

We propose N-block Modelsthe correlation of image blocks is the focus. the probabilities of a subset of keyblocks

distributed according to certain spatial configurations are used as feature vectors.


Bi-block Spatial Configurations

ck-1ck-1

c k-1c k-1

c k

c k

c kc k

horizontal

diagonal (minor)diagonal (main)

vertical


Tri-block Spatial Configurations

c k-2

c k

c k-2

c k-2

c k-2

c k-1

c k-1

c k-1

c k-1

c k

c k

c k

horizontal vertical

diagonal (main) diagonal (minor)


Tri-block Spatial Configurations

c k-2

c k-2 c k-2

c k-2

c k-1

c k-1

c k-1

c k-1

c k

c k

c k

c k

triangular configure 1 triangular configure 4

triangular configure 3 triangular configure 2


Multi-modal Image Retrieval

The above models capture different image content under various contexts. The single-block models only consider single keyblock's occurrence;The n-block models consider multiple keyblocks' co-occurrence.

If keyblocks of different size are used, image content in different granularity will be focused on.

Since each individual model can't satisfy all requirements of image content extraction and retrieval, it is necessary to combine them to improve the retrieval performance. Feature combinationResult fusion


keyblock-keyblock correlation matrix

keyblock-keyblock correlation matrixThe rows and columns are associated with the keyblocks in

the codebook C (|C| = N) Each item (ki,l) is a normalized correlation factor between

keyblock ci and cl

ni is the number of images which contain ci;

nl is the number of images which contain cl;

ni,l is the number of images which contain both ci and cl

lili

lili

nnn

nK

,

,,

nnliK )( ,


keyblock-keyblock correlation matrix

We can use the keyblock-keyblock correlation matrix to redefine the feature vector of the histogram model

fij is the frequency of keyblock ci appearing in image Ij and fij

* is the correlation weight calculated by combining frequencies of ci’s correlated keyblocks with their correlation factor together.

is a threshold (usually 0.3 0.5 ) to cut off the effects of those less correlated keyblocks.

lijl

lijljijijiji

KIcKffffw

,

,*,,,,, *


Region-based Image Retrieval

Keyblocks can be any image feature segments such as pixels, blocks and

regions, etc.

Regions Are better “keywords” because they usually carry more semantic

meanings and they are closer to the objects . Image segmentation is still a difficult problem.Segmentation

algorithms inevitably make some mistakes, e.g., over-segmentation.

How to effectively and efficiently extract region features?How to retrieve images based on region features and

corresponding region spatial constraints?



Images are segmented into several regions;Visual features are extracted for each

region; The image content is represented by the set

of region features;At the query time, the query image is

segmented into several regions. Then the features of one or more regions are matched against region features which represent images in the database.


Integrate Regions into Keyblock Framework

Keyblock framework is quite extensible; substitute blocks with regions in the whole framework

Segmentation : Expectation-Maximization (EM)proposed in the Blobworld system iteratively models the joint distribution of color and texture with a

mixture of Gaussians

Region featuresColor feature: color histogram of the pixels in the region. based on

the original keyblock representation (1x1, 128);Texture feature: the mean texture contrast and anisotropy of the

pixels in the region;Normalized area feature: the number of pixels of a region divided

by the image size.


Integrate Regions into Keyblock Framework

Shape features: X-axis and Y-axis profiles (10-dimension feature vector ) (1) Find the minimum bounding box B of the region; (2) Equally subdivide B along both X and Y axes into 5 intervals; (3) For each cell (u,v) obtained from the above subdivision,

calculate the percentage p(u,v) of the region that cell (u,v) contains; (4) Define the profile of the region along the X-axis as a 5-element

array x with the i-th element x(i) = 5v=1 p(i,v);

(5) Similarly define the profile of the region along the Y-axis as

a 5-element array y with the j-th element y(j) = 5v=1p(u,j).


Feature Combination Model

In the phase of feature extraction, for each image, combine feature vectors generated by different models into one comprehensive feature vector. Feature vectors

Model Model Combination Model where

or

),...,,...( Ni 1

),...,,...( Ni 1

),...,,...( Ni 1

),( iii

ww iii **


Result Fusion ModelIn the phase of retrieval, for each image, combine retrieval results under different models. <image, similarity> lists

Model Model Combination Model where

},,, ,...,,...{ MMjj SISISI 11

wSwSS iii **

},,, ,...,,...{ MMjj SISISI 11

},,, ,...,,...{ MMjj SISISI 11


Experiments on Test Databases

CDB (web color images)500 images , 41 groups, each group 10 or 20 images41 training images are randomly selectedquery set : whole databasecolor feature techniques: histogram and color coherent vector

(CCV)average precision and recall from 1 to 40 returned images are

calculated.TDB (Brodatz texture images)

2240 images , 112 groups, each group 20 images112 training images are randomly selectedquery set : whole databasetexture feature techniques : haar and daubechies waveletaverage precision and recall from 1 to 40 returned images are

calculated.


Experiments: comparison with traditional techniques


Performance of N-block Models

All the three n-block models achieve higher performance than thetraditional techniques, while the bi-block and uni-block models perform better on these two datasets.


Experiments on COREL

31646 color images size 120x80 or 80x120939 training images are randomly selected to get

keyblocks

query set6895 query images which are categorized to 82

groups.

average precision and recall from 1 to 100 returned images are calculated.


Experiments on COREL -- Performance Comparison

The performance of the keyblock approach outperforms the traditional techniques.


Experiments on GEO

Database GEO Airphoto images of the Buffalo area provided by NCGIA

at Buffalo405 images 46 training images are used to get keyblocks

Query set33 query images which are sub-images of 32 x 32 chosen

from the images in the database by GIS experts from NCGIA at Buffalo.

These query images are divided into 5 categories: agriculture, grass, forest, residential area, and water.


Experiments on GEO : comparison with wavelet

transforms


An Example Query


Experiments for Region-based Image Retrieval

Data set with 1004 images (14 categories)Group A : images with distinctive objects. (have better segmentation

results) Group B : images without distinctive objects.

Currently the segmentation results are not satisfactory due to the limitation of the algorithm as well as the intrinsic difficulties of image segmentation on natural images.

Segmentation result is critical, we expect that query results of Group A would be better than Group B.




Conclusion

We established a framework for browsing and navigating geographic images

We use effective metadata representation and management for integration of multiple data sources and provide efficient access to the data sources.

We developed wavelet-based approach and keyblock-based approach to generalize the text-based IR techniques to geographic image retrieval.

Many remaining research issues.

Documents

University at BuffaloThe State University of New York Keyblock Approach: Metadata Generation and Retrieval of Geographic Imagery Aidong Zhang Associate