The Twenty-Third IEEE Conference on Computer Vision and ...€¦ · Poster Spotlights The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition Session: Object Recognition,

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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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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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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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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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

0.3

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0.7

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


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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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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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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4

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Image

Pattern

Candidate window

Matched window

. . . . . . .. . . . . . . . . . . . . . . . . . . .

Pattern matching

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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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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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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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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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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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Documents

The Twenty-Third IEEE Conference on Computer Vision and ...€¦ · Poster Spotlights The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition Session: Object Recognition,