from pixels to semantics – research on intelligent image indexing and retrieval james z. wang pnc...
TRANSCRIPT
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From Pixels to Semantics –Research on Intelligent Image Indexing and Retrieval
James Z. WangPNC Technologies Career Dev. Professorship
School of Information Sciences and TechnologyThe Pennsylvania State University
http://wang.ist.psu.edu
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“Building, sky, lake, landscape, Europe, tree”
Poll: Can a computer do this?
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Outline
Introduction Our related SIMPLIcity work ALIP: Automatic modeling and
learning of concepts Conclusions and future work
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The field: Image Retrieval The retrieval of relevant images from an
image database on the basis of automatically-derived image features
Applications: biomedicine, homeland security, law enforcement, NASA, defense, commercial, cultural, education, entertainment, Web, ……
Our approach: Wavelets Statistical modeling Supervised and unsupervised learning… Address the problem in a generic way for
different applications
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Chicana Art Project, 1995
1000+ high quality paintings of Stanford Art Library Goal: help students and researchers to find visually
related paintings Used wavelet-based features [Wang+,1997]
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Feature-based Approach
+ Handles low-level semantic queries
+ Many features can be extracted
-- Cannot handle higher-level queries (e.g.,objects)
Signature
feature 1feature 2……
feature n
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Region-based Approach Extract objects from images first
+ Handles object-based queriese.g., find images with objects that are similar to some given objects
+ Reduce feature storage adaptively
-- Object segmentation is very difficult-- User interface: region marking, feature
combination
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UCB Blobworld[Carson+, 1999]
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Outline
Introduction Our related SIMPLIcity work ALIP: Automatic modeling and
learning of concepts Conclusions and future work
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Motivations
Observations: Human object segmentation relies on knowledge Precise computer image segmentation is a very difficult open
problem
Hypothesis: It is possible to build robust computer matching algorithms without first segmenting the images accurately
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Our SIMPLIcity Work [PAMI, 2001(1)] [PAMI, 2001(9)][PAMI, 2002(9)]
Semantics-sensitive Integrated Matching for Picture LIbraries
Major features Sensitive to semantics: combine
statistical semantic classification with image retrieval
Efficient processing: wavelet-based feature extraction
Reduced sensitivity to inaccurate segmentation and simple user interface: Integrated Region Matching (IRM)
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Wavelets
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Fast Image Segmentation
Partition an image into 4×4 blocks Extract wavelet-based features from each block Use k-means algorithm to cluster feature vectors into
‘regions’ Compute the shape feature by normalized inertia
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K-means Statistical Clustering
Some segmentation algorithms: 8 minute CPU time per image
Our approach: use unsupervised statistical learning method to analyze the feature space
Goal: minimize the mean squared error between the training samples and their representative prototypes
Learning VQ [Hastie+, Elements of Statistical Learning, 2001]
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IRM: Integrated Region Matching
IRM defines an image-to-image distance as a weighted sum of region-to-region distances
Weighting matrix is determined based on significance constrains and a ‘MSHP’ greedy algorithm
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A 3-D Example for IRM
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IRM: Major Advantages
1. Reduces the influence of inaccurate segmentation
2. Helps to clarify the semantics of a particular region given its neighbors
3. Provides the user with a simple interface
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Experiments and Results
Speed 800 MHz Pentium PC with LINUX OS Databases: 200,000 general-purpose image DB
(60,000 photographs + 140,000 hand-drawn arts)70,000 pathology image segments
Image indexing time: one second per image Image retrieval time:
Without the scalable IRM, 1.5 seconds/query CPU time With the scalable IRM, 0.15 second/query CPU time
External query: one extra second CPU time
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RANDOM SELECTION
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Current SIMPLIcity System
Query Results
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External Query
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Robustness to Image Alterations
10% brighten on average 8% darken Blurring with a 15x15 Gaussian filter 70% sharpen 20% more saturation 10% less saturation Shape distortions Cropping, shifting, rotation
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Status of SIMPLIcity Researchers from more than 40
institutions/government agencies requested and obtained SIMPLIcity
Where to find it -- do a google search of “image retrieval”
We applied SIMPLicity to: Automatic Web classification Searching of pathological and
biomedical images Searching of art and cultural images
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EMPEROR Database (C.-C. Chen, Simmons College)
terracotta soldiers of the First Emperor of China
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EMPEROR Project
C.-C. ChenSimmons College
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(1) Random Browsing
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(2) Similarity Search
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(2) Similarity Search
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(3) External Image Query
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Outline
Introduction Our related SIMPLIcity work ALIP: Automatic modeling and
learning of concepts Conclusions and future work
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Why ALIP?
Size 1 million images
Understandability &Vision “meaning” depend
on the point-of-view Can we translate
contents and structure into linguistic terms
dogs
Kyoto
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(cont.)
Query formulation SIMILARITY: look similar to a given
picture OBJECT: contains an explosive device OBJECT RELATIONSHIP: contains a
weapon and a person; find all nuclear facilities from a satellite picture
MOOD: a sad picture TIME/PLACE: sunset near the Capital
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Automatic Linguistic Indexing of Pictures (ALIP)
A new research direction Differences from computer vision
ALIP: deal with a large number of concepts ALIP: rarely find enough number of “good”
(diversified/3D?) training images ALIP: build knowledge bases
automatically for real-time linguistic indexing (generic method)
ALIP: highly interdisciplinary (AI, statistics, mining, imaging, applied math, domain knowledge, ……)
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Automatic Modeling and Learning of Concepts for Image Indexing
Observations: Human beings are able to build models about
objects or concepts by mining visual scenes The learned models are stored in the brain
and used in the recognition process Hypothesis: It is achievable for computers to
mine and learn a large collection of concepts by 2D or 3D image-based training
[Wang+Li, ACM Multimedia, 2002][PAMI 2003]
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Concepts to be Trained Concepts: Basic building blocks in
determining the semantic meanings of images
Training concepts can be categorized as: Basic Object: flower, beach Object composition:
building+grass+sky+tree Location: Asia, Venice Time: night sky, winter frost Abstract: sports, sadness High-level
Low-level
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Modeling/Profiling Artist’s Handwriting (NSF ITR)
Each artist has consistent as well as unique strokes, equivalent of a signature Rembrandt: swift, accurate brush Degas: deft line, controlled scribble Van Gogh: turbulent, swirling strokes, rich of
textures Asian painting arts (focus of ITR, started
8/2002) Potential queries
Find paintings with brush strokes similar to those of van Gogh’s
Find paintings with similar artist intentions
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Database: 1000+ most significant Asian paintingsQuestion: can we build a “dictionary” of different painting styles?
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Database: terracotta soldiers of the First Emperor of ChinaQuestion: can we train the computer to be an art historian?
C.-C. Chen, PITAC and Simmons
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System Design Train statistical models of a dictionary of
concepts using sets of training images 2D images are currently used 3D-image training can be much better
Compare images based on model comparison
Select the most statistical significant concept(s) to index images linguistically
Initial experiment: 600 concepts, each trained with 40 images 15 minutes Pentium CPU time per concept,
train only once highly parallelizable algorithm
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Training Process
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Automatic Annotation Process
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Training
Training images used to train the concept “male” with description “man, male, people, cloth, face”
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Initial Model: 2-D Wavelet MHMM [Li+, 1999]
Model: Inter-scale and intra-scale dependence States: hierarchical Markov mesh, unobservable Features in SIMPLIcity: multivariate Gaussian distributed
given states A model is a knowledge base for a concept
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2D MHMM
Start from the conventional 1-D HMM Extend to 2D transitions Conditional Gaussian distributed feature vectors Then add Markovian statistical dependence across resolutions Use EM algorithm to estimate parameters
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Annotation Process
Statistical significances are computed to annotate images
Favor the selection of rare words
When n, m >> k, we have
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Preliminary Results
Computer Prediction: people, Europe, man-made, water
Building, sky, lake, landscape,
Europe, tree People, Europe, female
Food, indoor, cuisine, dessert
Snow, animal, wildlife, sky,
cloth, ice, people
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More Results
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Results: using our own photographs
P: Photographer annotation Underlined words: words predicted by
computer (Parenthesis): words not in the learned
“dictionary” of the computer
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Preliminary Results on Art Images
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Classification of Painters
Five painters: SHEN Zhou (Ming Dynasty), DONG Qichang (Ming), GAO Fenghan (Qing), WU Changshuo (late Qing), ZHANG Daqian (modern China)
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Advantages of Our Approach
Accumulative learning Highly scalable (unlike CART, SVM,
ANN) Flexible: Amount of training depends
on the complexity of the concept Context-dependent: Spatial relations
among pixels taken into consideration Universal image similarity: statistical
likelihood rather than relying on segmentation
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Outline
Introduction Our related SIMPLIcity work ALIP: Automatic modeling and
learning of concepts Conclusions and future work
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Conclusions We propose a research direction:
Automatic Linguistic Indexing of Pictures Highly challenging but crucially important Interdisciplinary collaboration is critical
Our SIMPLIcity image indexing system Our ALIP System: Automatic modeling and
learning of semantic concepts 600 concepts can be learned automatically
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Future Work Explore new methods for better accuracy
refine statistical modeling of images learning from 3D refine matching schemes
Apply these methods to special image databases
(e.g., art, biomedicine) very large databases
Integration with large-scale information systems COMPLexity? COntent analysis for Manuscript
Picture Libraries ……
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Acknowledgments NSF ITR (since 08/2002) Endowed professorship from the PNC
Foundation Equipment grant from SUN Microsystems Penn State Univ.
Joint work: Prof. Jia Li, Penn State Statistics Earlier funding (1995-2000): IBM QBIC, NEC
AMORA, SRI AI, Stanford Lib/Math/Biomedical Informatics/CS, Lockheed Martin, NSF DL2
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More Information
http://wang.ist.psu.edu
Papers in PDF, image databases, downloads,demo, etc