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Discovering Collocation Patterns: from Visual Words to Visual Phrases. Junsong Yuan, Ying Wu and Ming Yang CVPR’07. Discovering Visual Collocation. An exciting idea: detour.

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discovering collocation patterns from visual words to visual phrases

Discovering Collocation Patterns:from Visual Words to Visual Phrases

Junsong Yuan, Ying Wu and Ming Yang

CVPR’07

an exciting idea detour
An exciting idea: detour
  • Related Work: J. Sivic et al. CVPR04, B. C. Russellet al. CVPR06, G. Wang et al. CVPR06, T. Quack et al. CIVR06, S. C. Zhu et al. IJCV05, …
confrontation
Confrontation
  • Spatial characteristics of images
    • over-counting co-occurrence frequency
  • Uncertainty in visual patterns
    • Continuous visual feature quantized word
    • Visual synonym and polysemy
selecting visual phrases
Selecting visual phrases
  • Visual collocations may occur by chance
  • Selecting phrases by a likelihood ratio test:
    • H0: occurrence of phrase P is randomly generated
    • H1: phrase P is generated by a hidden pattern
  • Prior:
  • Likelihood:
  • Check if words are co-located together by chance or statistically meaningful
discovery of visual phrases
Discovery of visual phrases

Closed

FIM

Frequent Word-sets ( |P|>=2 )

AB

AE

CD

CE

DE

AF

BE

BF

CDE

ABF

ABE

pair-wise student t-test

ranked by L(P)

Group Database

likelihood ratio

AB

15.7

14.3

12.2

10.9

9.7

AF

Visual Phrase Lexicon (VPL)

ABF

BF

CD

frequent itemset mining fim
Frequent Itemset Mining (FIM)
  • If an itemset is frequent  then all of its subsets must also be frequent
phrase summarization
Phrase Summarization
  • Measuring the similarity between visual phrases by KL-divergence Yan et al., SIGKDD 05
  • Clustering visual phrases by Normalized-cut
pattern summarization results
Pattern Summarization Results

Face database: summarizing top-10 phrases into 6 semantic phrase patterns

Car database: summarizing top-10 phrases into 2 semantic phrase patterns

partition of visual word lexicon
Partition of visual word lexicon
  • Metric learning method:
  • Neighborhood component analysis (NCA). Goldberger, et al., NIPS05
    • improve the leave-one-out performance of the nearest neighbor classifier
evaluation
Evaluation
  • K-NN spatial group: K=5
  • Two image category database: car (123 images) and face (435 images)
  • Precision of visual phrase lexicon
    • the percentage of visual phrases Pi ∈ Ψ that are located in the foreground object
  • Precision of background word lexicon
    • the percentage of background words Wi ∈ Ω−that are located in the background
  • Percentage of images that are retrieved:
results visual phrases from car category
Results: visual phrases from car category

Visual phrase pattern 1: wheels

different colors represent different semantic meanings

Visual phrase pattern 2: car bodies

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