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Jifeng Dai
2011/09/27
Introduction
Structural SVM
Kernel Design
Segmentation and parameter learning
Object Feature Descriptors
Experimental results
Conclusions and Future Work
CVPR 2011 Oral
Things to do:
Contributions:
1) Propose a kernelized structural support vector machine approach to learn discriminatively the mapping from image to a segmentation mask.
2) Combine high level object similarity information with multiple low level segmentation cues into a novel kernel.
3) Traditional segmentation regularizations are preserved in the framework and explicitly enforced during the learning process. This way smoothness of the solution does not need to be “re-learned” from training examples.
Complex output
The dog chased the catxS VPNP
Det NV
NP
Det N
y2
S VPVP
Det NV
NP
V N
y1
S
NPVP
Det NV
NP
Det N
yk
…
Training Examples:
Hypothesis Space:
The dog chased the catx
S VPNP
Det NV
NP
Det N
y1
S VPVP
Det NV
NP
V N
y2
S
NPVP
Det NV
NP
Det N
y58
S VPNP
Det NV
NP
Det N
y12
S VPNP
Det NV
NP
Det N
y34
S VPNP
Det NV
NP
Det N
y4
Training: Find that solve
Problems• How to predict efficiently?• How to learn efficiently?• Manageable number of parameters?
The idea behind Structured SVM is to discriminatively learn a scoring function over input/output pairs (i.e. over image/mask pairs).
Loss function:
Two important choices:1) Restrict the search to Ys, subset of Y
composed by smooth segmentation masks.
Two important choices:1) Restrict the search to Ys, subset of Y
composed by smooth segmentation masks.
2) using kernel functions so that we could work in the dual formulation.
HOG…
Object Similarity KernelMask Similarity Kernel
Mask Similarity Kernel
1) Shape Kernel
2) Local Color Model Kernel
3) Global Color Model Kernel
Graph cuts
Mask smooth term
In which
So (6) and (7) take the form:
Graph cuts!!!
Parameters are optimized on a validation set
HOG grid or detector response feature
Datasets:
1)the Dresses dataset (600 images)2)the Weizmann horses dataset (328 images)3)the Oxford 17 category flower dataset (849
images)
How to measure performance?
Comparison with previous works:
Comparison with previous works:
Comparison with previous works: Oxford Flower Dataset
Previous work:
Examples:
Examples:
Examples:
Contributions:
1) Propose a kernelized structural support vector machine approach to learn discriminatively the mapping from image to a segmentation mask.2) Combine high level object similarity information with multiple low level segmentation cues into a novel kernel.3) Traditional segmentation regularizations are preserved in the framework and explicitly enforced during the learning process. This way smoothness of the solution does not need to be “re-learned” from training examples.
Future Work:
1)Model the boundary curves (driven by low-level cues).
2) Instead of relying on a single global object similarity kernel, dividing the kernel into a parts-based representation.
3) Establish a theoretical connection between the complexity of the top-down models the algorithm can learn and the number of segmentations needed in the training set.