1 Feature Selection in Video Classification Yan Liu Computer Science Columbia University Advisor:...
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Feature Selection in Video Classification
• Yan Liu
• Computer Science
• Columbia University
• Advisor: John R. Kender
Ph.D. Thesis Proposal
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Outline
• Introduction
• Research progress
• Proposed work
• Conclusion and schedule
1. Introduction
2. Progress
3. Proposal
4. Conclusion
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1. Introduction
2. Progress
3. Proposal
4. Conclusion
Outline
• Introduction– Motivation of feature selection in video
classification
– Definition of feature selection
– Feature selection algorithm design and evaluation
– Applications of feature selection
• Research progress• Proposed work• Conclusion and schedule
1. Introduction
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1. Introduction1.1.Motivation1.2.Definition1.3.Components1.4. Applications 2. Progress
3. Proposal
4. Conclusion
Motivation of feature selection in video classification
• The problem of efficient video data management is an important issue – “Semantic gap”: machine learning methods,
such as classification can close it– Efficiency: reducing the dimensionality of the
data prior to processing is necessary
• Feature selection in video classification not well explored– So far, mostly based on researchers’ intuition [A.
Vailaya 2001]
– Goal: select representative features automatically
1.1. Motivation
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1. Introduction1.1.Motivation1.2.Definition1.3.Components1.4. Applications
2. Progress
3. Proposal
4. Conclusion
Definition of feature selection
• Feature selection focuses on– Finding a feature subset that has the most discriminative
information from the original feature space– Objective: [Guyon 2003]
• Improve the prediction performance• Provide a more cost-effective predictor• Provide a better understanding of the data
• Two major approaches define feature selection [Blum 1997]– Filter methods: emphasize the discovery of relevant
relationships between features and high-level concepts– Wrapper methods: seek a feature subset that minimizes
prediction error of classifying the high-level concept
1.2. Definition
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1. Introduction1.1.Motivation1.2.Definition1.3.Components1.4. Applications 2. Progress
3. Proposal
4. Conclusion
Feature selection algorithm design
1.2. Definition
f1, f2, f3, ………………………………….fN
S1{f1}, S2{f2},……..SN{fN}, SN+1{f1, f2} ……………S2N{f1, f2, ……..fN}
Original Feature Space:
Search Algorithm Induction Evaluation
Stop Point
Wrapper Filter
Feature subset Si {f1, f2, ……..fk}, 1 ≦ k N≦
Target Feature Space
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1. Introduction1.1.Motivation1.2.Definition1.3.Components1.4. Applications 2. Progress
3. Proposal
4. Conclusion
Three components of feature selection algorithms
1.3. Components
• Search algorithm– Forward selection [Singh 1995]
– Backward elimination [Koller 1996]
– Genetic algorithm [Oliveira 2001]
• Induction algorithm – SVM [Bi 2003], BN [Singh 1995], kNN [Abe 2002], NN
[Oliveira 2001], Boosting [Das 2001]
– Classifier-specific [Weston 2000] and classifier-independent feature selection [Abe 2002]
• Evaluation metric – Distance measure [H. Liu 2002], dependence measure,
consistency measure [Dash 2000], information measure [Koller 1996]
– Predictive accuracy measure (for most wrapper methods)
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1. Introduction1.1.Motivation1.2.Definition1.3.Components1.4. Applications
2. Progress
3. Proposal
4. Conclusion
Applying feature selection to video classification
1.4. Applications
• Current applications with large data sets– Text categorization [Forman 2003]
– Genetic microarray [Xing 2001]
– Handwritten digit recognition [Oliveira 2001]
– Web classification [Coetzee 2001]
• Applying existing feature selection algorithms to video data– Similar need: massive data, high
dimensionality, complex hypotheses– Difficulty: higher requirement of time cost– Some existing work in video classification
[Jaimes 2000]
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Outline
2. Progress
• Introduction • Research progress
– BSMT: Basic Sort-Merge Tree [Liu 2002]
– CSMT: Complement Sort-Merge Tree [Liu 2003]
– FSMT: Fast-converging Sort-Merge Tree [Liu 2004]
– MLFS: Multi-Level Feature Selection [Liu 2003]
– Fast video retrieval system [Liu 2003]
• Proposed work• Conclusion and schedule
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1. Introduction
2. Progress2.1. BSMT2.1.1. Search2.1.2. Induction2.1.3. Time cost2.1.4. Application2.1.5. Experiment2.2. CSMT2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Setup Basic Sort-Merge Tree
2.1.1.Search
• Initialize level = 1– N singleton feature subsets
• While level < log2 N
– Induce on every feature subset– Sort subsets based on their classification
accuracy– Combine, pair-wise, feature subsets
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1. Introduction
2. Progress2.1. BSMT2.1.1. Search2.1.2. Induction2.1.3. Time cost2.1.4. Application2.1.5. Experiment2.2. CSMT2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Search algorithm
2.1.1.Search
A1 (1) A2 A3 A4 A5 A6 A7 A8 A255 A256
B1(2) B2 B3 B4 B128
I1(256)Combine
Sort
Induce
Combine
Sort
Induce
Combine
Sort
Induce
C1(4) C2 C64
Low High
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1. Introduction
2. Progress2.1. BSMT2.1.1. Search2.1.2. Induction2.1.3. Time cost2.1.4. Application2.1.5. Experiment2.2. CSMT2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Advantages
2.1.1.Search
• To achieve better performance– Avoids local optima of forward selection and
backward elimination
– Avoids heuristic randomness of genetic algorithms
• To achieve lower time cost– Search algorithm is linear in the number of
features – Enables the straightforward creation of near-
optimal feature subsets with little additional work [Liu 2003]
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1. Introduction
2. Progress2.1. BSMT2.1.1. Search2.1.2. Induction2.1.3. Time cost2.1.4. Application2.1.5. Experiment2.2. CSMT2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Induction algorithm
2.1.2.Induction
• Novel combination of Fastmap and Mahalanobis likelihood
• Fastmap for dimensionality reduction [Faloutsos 1995]
– Feature extraction algorithm approximates PCA with linear time cost
– Reduces the dimensionality of feature subsets to a pre-specified small number
• Mahalanobis maximum likelihood for classification [Duda 2000]
– Computes the likelihood that a point belongs to a distribution that is modeled as a multidimensional Gaussian with arbitrary covariance
– Works well for video domain
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1. Introduction
2. Progress2.1. BSMT2.1.1. Search2.1.2. Induction2.1.3. Time cost2.1.4. Application2.1.5. Experiment2.2. CSMT2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Applications to instructional video frame categorization
2.1.4. Application
• Pre-processing:– Temporally subsample: every other I frame (one
frame/sec)
– Spatially subsample: six DC terms of each macro-block
• Feature selection– From 300 six-dimensional features to r features
• Video segmentation and retrieval– Classify frames or segments in the usual way
using the resulting feature subset
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1. Introduction
2. Progress2.1. BSMT2.1.1. Search2.1.2. Induction2.1.3. Time cost2.1.4. Application2.1.5. Experiment2.2. CSMT2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Test bed of instructional video frame categorization
2.1.5. Experiment
• Classify instructional video of a 75 minute lecture in MPEG-1 format– 4700 video frames with 300 six-dimensional features– 400 training data for feature selection and classification
training– Classify to four categories
• Benchmarks: Random feature selection for 100 times– Experiments differ only in selected features– Any other benchmark is intractable on video dataset
Handwriting Announcement Demo Discussion
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1. Introduction
2. Progress2.1. BSMT2.1.1. Search2.1.2. Induction2.1.3. Time cost2.1.4. Application2.1.5. Experiment2.2. CSMT2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Accuracy improvement
2.1.5. Experiment
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1 2 3 4 5 6 7 8 9 10
Fastmap dimensions c from 1 to 10
Err
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rate
Mean of Random Sort-Merge
Comparison of frame categorization error rate using 30 (of 300) features selected by BSMT: nearly perfect!
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1. Introduction
2. Progress2.1. BSMT2.1.1. Search2.1.2. Induction2.1.3. Time cost2.1.4. Application2.1.5. Experiment2.2. CSMT2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Test bed of sports video retrieval
2.1.5. Experiment
Pitching Part Competing image types
• Retrieve “pitching” frames from an entire video
– Sampled more finely: every I frame – 3600 frames for half an hour
• First task: binary classify 3600 video frames• Second task: retrieve 45 “pitching” segments
from 182 pre-segmented video segments
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1. Introduction
2. Progress2.1. BSMT2.1.1. Search2.1.2. Induction2.1.3. Time cost2.1.4. Application2.1.5. Experiment2.2. CSMT2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Accuracy improvement
2.1.5. Experiment
Fixed Fastmap dimension: c = 2 Different sample rate r: from 2 to 32
Precision: percentage of items classified as positive that actually are positive (left bars in graphs)Recall: percentage of positives that are classified as positives (right bars in graphs)
r = 2 r = 8 r = 16 r = 32
Feature number r = 8
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Precision Recall
Mean of Random Sort-Merge
Feature number r = 16
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Mean of Random Sort-Merge
Feature number r = 32
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Mean of Random Sort-Merge
Feature number r = 2
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Mean of Random Sort-Merge
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.2.1. Sparse train2.2.2. Search2.2.3. Experiment2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Adapting to sparse training data
2.2.1. Sparse train
• Difficulties of feature selection in video, as in genetic microarray data– Huge feature sets with 7130 dimensions – Small training data sets with 38 training data
• Renders some feature selection algorithm ineffective [Liu 2003]– More coarsely quantized prediction error– Randomness accumulates with the choice of each feature,
influencing the choice of its successors • Existing feature selection for microarray data is a model for
video [Xing 2001]– Forward search based on information gain: 7130 features
reduced to 360– Backward elimination based on Markov Blanket: 360 features
reduced to 100 or less– Leave-one-out cross-validation decides the best size of the
feature subset
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.2.1. Sparse train2.2.2. Search2.2.3. Experiment2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Complement Sort-Merge Tree for video retrieval
2.2.1. Sparse train
• Focuses on sparse and noisy training data in video retrieval
• Combines the performance guarantees of a wrapper method with the logical organization of a filter method– Outer wrapper model for high accuracy
– Inner filter method to merge the feature subsets based on “complement” requirement, addressing the limitation of sparse training data
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.2.1. Sparse train2.2.2. Search2.2.3. Experiment2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Search algorithm
2.2.2. Search
Illustration of CSMT for N = 256.
Complement
Sort
Induce
Complement
Sort
Induce
Complement
Sort
Induce
A1 A2 A5 A6 A255 A256
C1 C2 C64
B1 B2 B3 B4 B128
I1
• Leaves: singleton feature subsets;
• White nodes: unsorted feature subsets
• Gray nodes: white nodes rank-ordered by performance.
• Black nodes: pairwise mergers of gray nodes, with pairs formed under the complement requirement.
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.2.1. Sparse train2.2.2. Search2.2.3. Experiment2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Complement test
2.2.2. Search
Illustration of complement test of CSMT for the A level of last slide
• Suppose the sorted singletons A1’ and A3’are paired to form pair B1
• To finding a paring for A2’, examine A4,’ A5’, A6’, which have the same error rate on the m training samples
• The bitwise OR of performance vectors of A2’ and A5’ maximizes the performance coverage
• Therefore, A5’ complements A2’ for B2
Sort
Induce
Complement
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A1 A2 A3 A4 A5 A6 A7
B1 B2 B3
A1’ A2’ A3’ A4’ A5’ A6’ A7’
A1’’ A2’’ A3’’ A4’’ A5’’ A6’’ A7’‘
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.2.1. Sparse train2.2.2. Search2.2.3. Experiment2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Test bed for video retrieval
2.2.3. Experiment
• Retrieve “announcements” frames from an entire video with 4500 frames
• No prior temporal segmentation or other pre-processing.
•
• Only 80 training data, including considerable noise:
Announcement Competing image types
Clean training data But also, noisy training data
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.2.1. Sparse train2.2.2. Search2.2.3. Experiment2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Accuracy improvement
2.2.3. Experiment
• Fixed sample rate: r = 8
• Different dimension c: from 1 to 10
• Same dimension c = 4
• Different sample rate r: from 2 to 16
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Fastmap dimension c (1~10)
Err
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Mean of Random CSMT
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Number of featuresE
rro
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Mean of Random CSMT
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.2.1. Sparse train2.2.2. Search2.2.3. Experiment2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Test bed of shot classification
2.2.3. Experiment
• Retrieve “emphasis” frames in an instructional video of MPEG-1 format– More subtle, semantically-defined class– Segment into 69 shots
• 3600 test data ( i.e. every I frame of a 60 minute video)
• 200 training data, from 10 example sequences
Emphasis part Non-emphasis part
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.2.1. Sparse train2.2.2. Search2.2.3. Experiment2.3. FSMT2.4. Retrieval 3. Proposal
4. Conclusion
Accuracy improvement
2.2.3. Experiment
Error rate of CSMT vs. random for retrieval of “emphasis” with features r fixed at 16.
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.3. FSMT2.3.1. Search2.3.2. Experiment2.4. Retrieval 3. Proposal
4. Conclusion
Fast-converging Sort-Merge Tree
2.3. FSMT
• Focuses on the problem of – Over-learning data sets– On-line retrieval requirement
• Sets up selected parts of the feature selection tree to save time, without sacrificing accuracy
• Uses information gain as an evaluation metric, instead of prediction error in BSMT
• Controls the convergence speed (amount of pruning at each level ) based on user’s requirement
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.3. FSMT2.3.1. Search2.3.2. Experiment2.4. Retrieval 3. Proposal
4. Conclusion
Search algorithm
2.3.1. Search
• Initialize level = 0– N singleton feature subsets.– Calculate R: number of features retained at
each level, based desired convergence rate and the goal of r features at conclusion.
• While level < log2 r +1 – Induce on every feature subset.– Sort subsets based on information gain.– Prune the level based on R.– Combine, pair-wise, feature subsets form
those remaining.
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.3. FSMT2.3.1. Search2.3.2. Experiment2.4. Retrieval 3. Proposal
4. Conclusion
FSMT of constant converge rate
2.3.1. Search
E1 E2
D1 D2 D8
C1 C2 C32
B1 B2 B128
A1 A2 A512
F1 F2 F1800
v0=1
r1 = 16v1 = 2r2 = 8v2 = 8r3 = 4v3 = 32r4 = 2v4 = 128r5 = 1v5 = 512
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.3. FSMT2.3.1. Search2.3.2. Experiment2.4. Retrieval 3. Proposal
4. Conclusion
Accuracy improvement
2.3.2. Experiment
• Same test bed with BSMT
• 1800 single-dimensional features
• Better performance than random feature selection and similar performance with CSMT ( r = 16 )
• The number of inductions is only 682 using FSMT, compared with 4095 using BSMT or CSMT
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1 2 3 4 5 6 7 8 9 10
Fastmap dimension c (1~10)
Mean of Random FSMT1 FSMT2
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.3. FSMT2.3.1. Search2.3.2. Experiment2.4. Retrieval 3. Proposal
4. Conclusion
Stable performance
2.3.2. Experiment
Although FSMT is an application-driven algorithm, it does retain some of the advantages of BSMT.
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Number of f eat ur es
Mean of Random FSMT
• Fixed sample rate: r = 8
• Different dimension c: from 1 to 10.
• Same dimension: c = 4
• Different sample rate r: from 2 to 16.
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.3. FSMT2.4. Retrieval2.4.1. MLFS2.4.2. Lazy Eval. 2.4.3. Experiment 3. Proposal
4. Conclusion
Coarse-fine scene segmentation using Multi-Level Feature Selection
2.4.1. MLFS
The feature subset hierarchy enables less work to be done on the segment interiors, and more costly feature subsets at segment edges.
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.3. FSMT2.4. Retrieval2.4.1. MLFS2.4.2. Lazy Eval. 2.4.3. Experiment 3. Proposal
4. Conclusion
How to define the parameters
2.4.1. MLFS
• Ri: Feature subset size (2, 4, 8 , . . .)
– Increases with i and therefore increases the classification accuracy
• Li: Neighborhood parameter (uncertainty region in
video)
– Remains constant or decreases with i and therefore focuses the attention of the more costly classifier
• Si: Decision threshold (of certainty of segmentation)
– Si = Pr(Cj) - Pr(Ck) k = 1, 2 … n and k j
– Pr(Cj) is the maximum Mahalanobis likelihood among
all categories using this feature subset
– Ensures that classification is correct and unambiguous
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.3. FSMT2.4. Retrieval2.4.1. MLFS2.4.2. Lazy Eval. 2.4.3. Experiment 3. Proposal
4. Conclusion
Lazy evaluation of on-line queries
2.4.2. Lazy Eval.
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.3. FSMT2.4. Retrieval2.4.1. MLFS2.4.2. Lazy Eval. 2.4.3. Experiment 3. Proposal
4. Conclusion
Efficiency improvement
2.4.3. Experiment
• Using same test bed of BSMT
• Only 3.6 features are used per frame
• With similar performance of 30 features selected by BSMT
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1. Introduction
2. Progress2.1. BSMT2.2. CSMT2.3. FSMT2.4. Retrieval
3. Proposal
4. Conclusion
Summary of current research progress
2. Progress
BSMT
CSMT
FSMT
MLFS
Sparse Training Data
Over-learned Data
Fast retrieval
Scene segment
Scene categorization
Recursive nature
Low-time cost
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1. Introduction
2. Progress
3. Proposal
4. Conclusion
Outline
3. Proposal
• Introduction• Research progress• Proposed work
• Improve current feature selection algorithm
• Algorithm evaluation
• New Applications
• Size of training data
• Theoretical analysis
• Conclusion and schedule
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1. Introduction
2. Progress
3. Proposal3.1. Improve3.2. Evaluation3.3. Application3.4. Train size3.5. Theory
4. Conclusion
Improvements to current algorithms
3.1. Improve
• Search algorithm– Set up the bottom of the Sort-Merge Tree more
efficiently
• Induction algorithm of feature selection– Explore SVM: powerful for binary
classification and sparse training data– Explore HMM: good performance in temporal
analysis
• Evaluation metric– Explore filter evaluation metric in the wrapper
method
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1. Introduction
2. Progress
3. Proposal3.1. Improve3.2. Evaluation3.3. Application3.4. Train size3.5. Theory
4. Conclusion
Algorithm evaluation
3.2. Evaluation
• Accuracy– Classification evaluation: Error rate, Balanced Error
Rate (BER), Received Operating Characteristic (ROC) Curve, Area Under Curve (AUC)
– Video analysis evaluation: Precision, Recall, F-measure
• Efficiency – Selected feature subsets size: Fraction of Features (FF),
best size of feature subset– Time cost: of search algorithm, of induction algorithm;
stopping point
• Dependence– How to choose proper classifier– How to compare feature selection algorithms in certain
applications
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1. Introduction
2. Progress
3. Proposal3.1. Improve3.2. Evaluation3.3. Application3.4. Train size3.5. Theory
4. Conclusion
Dimensions to compare different algorithms
3.2. Evaluation
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1. Introduction
2. Progress
3. Proposal3.1. Improve3.2. Evaluation3.3. Application3.4. Train size3.5. Theory
4. Conclusion
New applications
3.3. Applications
• Different original feature space– Feature fusion: put different kinds of features in one
feature space– High-level semantic features – Temporal-spatial information
– Different operates for different kinds of videos based on subject quality measurement [Y. Wang 2004]
– Content-based video compression– On-the-fly search
• Feature selection in video clustering – Forward wrapper method to select features and filter
method to remove redundant ones [Xie 2003]
42
1. Introduction
2. Progress
3. Proposal3.1. Improve3.2. Evaluation3.3. Application3.4. Train size3.5. Theory
4. Conclusion
Extension to training data set extremes
3.4. Train size
• Sparse training data– Better feature selection algorithms – Use training data efficiently in cross-validation
• Massive training data– Random selection based on two assumptions: feature subset performance
stability, and training set independence– Methods to extract a representative training data subset for feature selection
• Non-balanced training data– Positive examples are sparse, negative examples are massive and must be
sampled – Video retrieval in large database is non-balanced
• One class training data– Feature selection for one class training in masquerade (computer security
violation) detection [K. Wang 2003]– Select features using one class training in video retrieval
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1. Introduction
2. Progress
3. Proposal
4. Conclusion
Outline
4. Conclusion
• Introduction
• Research progress
• Proposed work
• Conclusion and schedule– Finished work and further work– Schedule
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Finished work and further work
Feature Selection Applications
Search Algorithm
CSMT
BSMT
MLFS
FSMT
Induction Algorithm
Evaluation Metric
Algorithm Evaluation
Challenge
discussionStop point
Over-fitting
Training dataset
Retrieval
Video
Classification
Categorization
Segmentation
Clustering
CompressionOther
applications
New feature setOn-the-fly
search
Theoretical Analysis
Gene microarray
Audio
Mostly done
Partly done
Mostlyundone
Half done
45
1. Introduction
2. Progress
3. Proposal
4. Conclusion4.1. Conclusion4.2. Schedule
Task Schedule
4.2. Schedule