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Inferring Semantic Concepts from Community- Contributed Images and Noisy Tags. Jinhui Tang † , Shuicheng Yan † , Richang Hong † , Guo -Jun Qi ‡ , Tat- Seng Chua † † National University of Singapore ‡ University of Illinois at Urbana-Champaign. Outline. Motivation

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inferring semantic concepts from community contributed images and noisy tags

Inferring Semantic Concepts from Community- Contributed Images and Noisy Tags

Jinhui Tang†, Shuicheng Yan †, Richang Hong †, Guo-Jun Qi ‡, Tat-Seng Chua †

† National University of Singapore

‡ University of Illinois at Urbana-Champaign

outline
Outline
  • Motivation
  • Sparse-Graph based Semi-supervised Learning
  • Handling of Noisy Tags
  • Inferring Concepts in Semantic Concept Space
  • Experiments
  • Summarization and Future Work
our task
Our task

No manual annotation are required.

methods can be used
Methods Can be Used
  • With models:
    • SVM
    • GMM
  • Infer labels directly:
    • k-NN
    • Graph-based semi-supervised methods
normal graph based methods
Normal Graph-based Methods
  • A common disadvantage:
    • Have certain parameters that require manual tuning
    • Performance is sensitive to parameter tuning
  • The graphs are constructed based on visual distance
    • Many links between samples with unrelated-concepts
    • The label information will be propagated incorrectly.
  • Locally linear reconstruction:
    • Still needs to select neighbors based on visual distance
key ideas of our approach
Key Ideas of Our Approach
  • Sparse Graph based Learning
  • Noisy Tag Handling
  • Inferring Concepts in the Concept Space
why sparse graph
Why Sparse Graph ?
  • Human vision system seeks a sparse representation for the incoming image using a few visual words in a feature vocabulary. (Neural Science)
  • Advantages:
    • Reducethe concept-unrelated links to avoid the propagation of incorrect information;
    • Practical for large-scale applications, since the sparse representation can reduce the storage requirement and is feasible for large-scale numerical computation.
normal graph v s sparse graph
Normal Graph v.s. Sparse Graph

Normal Graph Construction.

Sparse Graph Construction.

sparse graph construction
Sparse Graph Construction
  • The ℓ1-norm based linear reconstruction error minimization can naturally lead to a sparse representation for the images *.
  • The sparse reconstruction can be obtained by solving the following convex optimization problem:

minw||w||1 , s.t.x=Dw

w ∈ Rn : the vector of the reconstruction coefficients;

x∈ Rd : feature vector of the image to be reconstructed;

D∈ Rd*n (d < n) : a matrix formed by the feature vectors of the other images in the dataset.

* J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma. Robust face recognition via sparse representation. IEEE Transaction on Pattern Analysis and Machine Intelligence, 31(2):210–227, Feb. 2009

sparse graph construction cont
Sparse Graph Construction (cont.)
  • Handle the noise on certain elements of x:
    • Reformulate x = Dw+ξ, where ξ ∈ Rd is the noise term.
    • Then :
  • Set the edge weight of the sparse graph:
semi supervised inference cont
Semi-supervised Inference (cont.)
  • The problem with :
    • Muu is typically very large for image annotation
    • It is often computationally prohibitive to calculate its inverse directly
    • Iterative solution with non-negative constraints:
    • may not be reasonable since some samples may have negative contributions to the other samples
  • Solution:
    • Reformulate:
  • The generalized minimum residual method (usually abbreviated as GMRES) can be used to iteratively solve this large-scale sparse system of linear equations effectively and efficiently.
different types of tags
Different Types of Tags

√: correct; ?: ambiguous; m: missing

handling of noisy tags
Handling of Noisy Tags
  • We cannot assume that the training tags are fixed during the inference process.
  • The noisy training tags should be refined during the label inference.
  • Solution: adding two regularization terms into the inferring framework to handle the noise:
handling of noisy tags cont
Handling of Noisy Tags (cont.)
  • Solution:
    • Set the original label vector as the initial estimation of ideal label vector, that is, set , and then solve

and we can obtain a refined fl.

    • Fix fl and solve
    • Use the obtained to replace the y in the previous graph-based method, and we can solve the sparse system of linear equations to infer the labels of the unlabeled samples.
why concept space
Why Concept Space?
  • It is well-known that inferring concepts based on low-level visual features cannot work very well due to the semantic gap.
  • To bridge this semantic gap
    • Construct a concept space and then infer the semantic concepts in this space.
    • The semantic relations among different concepts are inherently embedded in this space to help the concept inference.
the requirements for the concept space
The requirements for the concept space
  • Low-semantic-gap: Concepts in the constructed space should have small semantic gaps;
  • Informative: These concepts can cover the semantic space spanned by all useful concepts (tags), that is, the concept space should be informative;
  • Compact: The set including all the concepts forming the space should be compact (i.e., the dimension of the concept space is small).
concept space construction
Concept Space Construction
  • Basic terms:
    • Ω : the set of all concepts;
    • Θ : the constructed concept set.
  • Three measures:
    • Semantic Modelability: SM(Θ)
    • Coverage of Semantic Concept Space: CE(Θ, Ω)
    • Compactness: CP(Θ)=1/#(Θ)
  • Objective:
solution for concept space construction
Solution for Concept Space Construction
  • Simplification: fix the size of the concept space.
  • Then we can transform this maximization to a standard quadratic programming problem.
  • See the paper for more details.
inferring concepts in concept space
Inferring Concepts in Concept Space
  • Image mapping: xi D(i)
  • Query concept mapping: cxQ(cx)
  • Ranking the given images:
experiments
Experiments
  • Dataset
    • NUS-WIDE LiteVersion (55,615 images)
  • Low-level Features
    • Color Histogram (CH) and Edge Direction Histogram (EDH), combine directly.
  • Evaluation
    • 81 concepts
    • AP and MAP
experiments1
Experiments

Ex1: Comparisons among Different Learning Methods

experiments2
Experiments

Ex1: Comparisons among Different Learning Methods

experiments3
Experiments
  • Ex2: Concept Inference with and without Concept Space
experiments4
Experiments

Ex3: Inference with Tags vs. Inference with Ground-truth

We can achieve an MAP of 0.1598 by inference from tags in the concept space, which is comparable to the MAP obtained by inference from ground-truth of training labels.

summarization
Summarization
  • Exploited the problem of inferring semantic concepts from community-contributed images and their associated noisy tags.
  • Three points:
    • Sparse graph based label propagation
    • Noisy tag handling
    • Inference in a low-semantic-gap concept space
future work
Future Work
  • Training set construction from the web resource
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