Poster SpotlightsThe Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition
Session: Thursday Poster Session, Thurs 17 June 2010, 10:30 - 12:10 am
ARISTA - Image Search to Annotation on Billions of Web Photos
Xin-Jing Wang, Lei Zhang, Ming Liu, Yi Li, Wei-Ying Ma
ARISTA - Image Search to Annotation on Billions of Web Photos
Duplicate search is a well-defined problem. Frequent terms/phrases indicate semantics.
When DB size increases, so does avg. recall. Avg. precision converges when DB size > 300M
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Poster SpotlightsThe Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition
Session: Object Recognition, Thu 17 June 2010, 10:30-12:30 pm
Breaking the interactive bottleneck in multi-class classification with active selection and binary
feedback
Ajay J. Joshi1 , Fatih Porikli2, and Nikolaos Papanikolopoulos1
1Univ. of Minnesota 2Mitsubishi Electric Research Labs
Multi-class active learning with binary feedback
Traditional annotation method: provide labels from huge pools of categories
What if there are thousands of classes?
How to handle an unknown number?
What if more classes appear over time?
Instead: learn multi-class classifiers seamlessly with only yes / no input
We propose a Value-of-Information approach to active selection
Advantages:
Allows much faster annotation, saving user timer
Allows dynamic data (increasing categories over time)
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Poster SpotlightsThe Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition
Session: Object Recognition V, Thu 17 June 2010, 10:30-12:10
Efficient Histogram-Based Sliding Window
Yichen Wei and Litian Tao
Efficient Histogram-Based Sliding Window
• Simultaneous histogram construction and bin-additive function evaluation
image
histogramindex map
changed bins += _
3 histogram bins are changed
• Constant complexity in histogram dimension
• Up to hundreds oftimes faster in• Object detection• Object tracking• Saliency analysis
= +_changed pixels
_
16 pixels are changed
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Poster SpotlightsThe Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition
Session: Object Recognition V, Thu 17 June 2010, 10:30a-12:10p
Pareto-optimal Dictionaries for Signatures
Michael Calonder, Vincent Lepetit, Pascal Fua
Pareto-optimal Dictionaries for Signatures
SignatureDictionaryKeypoint Combinatorial Problem
about 200500 or 1000
Multi-objective Genetic Algorithm
Pareto-front
Optimal accuracy/efficiency dictionaries
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Poster SpotlightsThe Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition
Session: Object Recognition V, Thu 17 June 2010, 10:30-12:10am
Region Moments: Fast invariant descriptors for detecting small image structures
Gianfranco Doretto and Yi YaoVisualization and Computer Vision Lab, GE Global Research
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
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0.8
0.9
1
False positive rate
True
pos
itive
rate
RCCMCMIRM
0 0.05 0.10.9
0.95
1
Region Moments:
45 d
egre
es65
deg
rees
90 d
egre
es
Central Moment Invariants Radial Moments
Green: Correct detections Yellow: Missed detections Red: False ala
APLICATIONAppearance-based detection of small image structures (e.g. vehicles in aerial video)
PROBLEM Design fast, rotation and scale invariant appearance descriptors
APPROACH Invariant image features design Invariant image moments design Speed
Linear classification Integral representation
Fast invariant descriptors for detectingsmall image structures9
Poster SpotlightsThe Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition
Session: Object Recognition V –Thu June 17, 2010, 10:30-12:30
Optimizing One-Shot Recognition with Micro-Set Learning
Kevin D. Tang, Marshall F. Tappen,Rahul Sukthankar, Christoph H. Lampert
Optimizing One-Shot Recognition with Micro-Set Learning
How best to exploit knowledge about common classes to identify rare ones?
Learn an internal representation that focuses on one-shot recognition in the training phase
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Poster SpotlightsThe Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition
Far-sighted Active Learning on a Budget for Image and Video
Recognition
Sudheendra Vijayanarasimhan, Prateek Jain and Kristen Grauman
Session: Object Recognition V, Thu, June 17, 2010 , 10:30 - 12:10
Our Approach Results
• Margin-based selection criterion• Alternating continuous optimization
• Applied to action recognition, object recognition, and CBIR.
• Outperforms passive and myopic active approaches.
Problem: Cost-sensitive Active Learning on a Budget
Far-sighted Active Learning on a Budget for Image and Video Recognition
Sudheendra Vijayanarasimhan, Prateek Jain, Kristen Grauman
$$
$$ $
Unlabeled data
Labeled data
Current Model Budget
$T
$$$
Budgeted BatchActive Selection
$
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Poster SpotlightsThe Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition
Session: Object Recognition V, Thu, June 17, 2010 , 10:30 - 12:10
Fast pattern matching using orthogonal Haar transform
Wanli Ouyang, Renqi Zhang, Cham Wai-KuenThe Chinese University of Hong Kong
Fast pattern matching using orthogonal Haar transform
A data structure that computes sum of pixels in a rectangle by 1 addition.
A transform that requires O(logu)additions per pixel to project N1xN2input window onto u basis vectors.
• Find the same result as Full Search (FS).• Up to 800 speed-up over full search.
speed-up=FS
Ours
TimeTime
N2
(0,0)j2
j1
(j1, j2, N2) (j1+N1, j2, N2)
N1
RectSum( j1, j2, N1, N2)
+- +StripSum
1 -1 0
0
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. . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Image
Pattern
Candidate window
Matched window
. . . . . . .. . . . . . . . . . . . . . . . . . . .
Pattern matching
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Poster SpotlightsThe Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition
Session: Object Recognition V , Thu 17 June 2010, 10:30 - 12:10 pm
One-Shot Multi-Set Non-rigid Feature-Spatial Matching
Marwan Torki and Ahmed Elgammal
One-Shot Multi-Set Non-rigid Feature-Spatial MatchingGoal: spatial consistent feature matching problem without solving Quadratic Assignment Contributions:1) An embedding framework for matching feature descriptors while preserving their spatial structure 2) Scalability: Linear approach, no quadratic assignment is needed3) Matching Multiple sets in one shot
Experimental Evaluation:1) Non-Rigid motions: walking, Handwaving,…2) Matching within class variation (Motorbikes, Airplanes,…3) Wide Baseline Matching “Hotel Sequence” up to 100% accuracy4) Different viewing conditions using INRIA datasets
Matching Settings:1)Pairwise Matching (PW).2)Multiset Pairwise Matching (MPW): Embed all features from all sets and use this global information for better pairwise matching.3)Multiset Clustering (MC): Cluster in the embedding space to achieve multiset matches.
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Poster SpotlightsThe Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition
Session: Object Recognition V, Thu 17 June 2010, 10:30-12:10am
Relaxing the 3L algorithm for an accurate implicit polynomial fitting
Moahammad Rouhani, Angel D. SappaComputer Vision Center – Barcelona, Spain
Relaxing the 3L algorithm for an accurate implicit polynomial fitting
Data fitting with implicit functions: amx Tf =)(
aMMa ΓΓ= TTE
+
−=
=
+
−
Γ
Γ
Γ
c0c
bMMM
M ,03
δ
δ
L
The
3L a
lgor
ithm
3L algorithm Proposed Alg.
)()( tnpftg ii +=
δδ 2)()( ipfg ∇±≈±
Relaxing the 3L algorithm
Algebraic criterion:
M. Rouhani & A. Sappa Computer Vision Center – Barcelona, Spain
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Poster SpotlightsThe Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition
Session: Object Recognition V, Thu 17 June 2010, 10:30-12:10 pm
Online Visual Vocabulary Pruning Using Pairwise Constraints
Pavan Mallapragada, Rong Jin and Anil K. Jain
Online Visual Vocabulary Using Pairwise Constraints
Images represented using pruned vocabulary.
80% reduction in the number of visual words. Features are evaluated using binary clustering tasks. Pruning improved computational and clustering performance.
• Visual vocabularies built from image databases are of large size.• All visual words may not be relevant to a particular task. • Can we prune the visual words to obtain task specific vocabulary using pairwise
must-link or cannot-link constraints?
Visual words that do not explain the similarity between the images.
Online Vocabulary Pruning using Group-LASSO
Similarity computed using pruned vocabulary
must reflect the user provided must-link or
cannot-link constraints.
User labeled the pair similar
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Poster SpotlightsThe Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition
Safety in Numbers: Learning Categories from Few Examples with
Multi Model Knowledge Transfer
T. Tommasi, F. Orabona, B. Caputo
Session: Object Recognition V, Thu 17 June 2010, 10:30-12:10 pm
Safety in Numbers: Learning Categories from Few Examples with Multi Model Knowledge Transfer
Goal: Learning a category from few examples.
Intuition: Use prior knowledge to boost learning.
Approach: - Discriminative;- Smart initialization based on prior knowledge.
Implementation: - Least Square Support Vector Machine;- Leave One Out Error, closed form.
Optimal Performance One-shot learning
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Poster SpotlightsThe Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition
Session: Object Recognition V, Thurs 17 June 2010, 10:30-12:10 pm
Rapid and Accurate Developmental Stage Recognition of C. elegans from High-
Throughput Image DataA.G. White, P.G. Cipriani, H.L. Kao, B.
Lees, D. Geiger, E. Sontag, K.C. Gunsalus, F. Piano.
Rapid and Accurate DEVelopmental STAge Recognition from C. elegans high-throughput image data
Separate and labelSegment
Manual countingAlgorithm
15C 20C 22.5C 25C
AdultLarvaeEmbryo
Em
bryo
nic
Leth
ality
100%
15C 20C 22.5C 25C
0%
DevStaR’s automated scoring is comparable to manual counting of developmental stages
Scoring
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