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Data Mining, 2008. ICDM '08. Eighth IEEE International Conference on
Duy-Dinh Le National Institute of Informatics2-1-2 Hitotsubashi, Chiyoda-ku
Tokyo, JAPAN [email protected]
Shin’ichi SatohNational Institute of Informatics2-1-2 Hitotsubashi, Chiyoda-ku
Tokyo, JAPAN [email protected]
Student: Tu , Chien-Hsun 69821059 LIU, Yuan-Ming 69821039
OutlineIntroductionProposed Framework -Face Processing
-Ranking by Local Density Score -Ranking by Bagging of SVM ClassifierExperimental ResultsConclusion
IntroductionLarge image and video databases have
become more available than ever to users.
This trend has shown the need for effective and efficient tools for indexing and retrieving based on visual content.
IntroductionImprove the retrieval performance is to take
into account visual information present in the retrieved faces.
Challenge - Facial appearance due to pose changes,
illumination ,facial expressions make face recognition difficult.
- No labels makes supervised and unsupervised learning methods inapplicable.
System Framework
Proposed Framework- Face Processing We perform a ranking process and learning of
person X’s model as follows:
Step 1: Detect faces and eye positions, and then perform face normalizations.
Proposed Framework
Step 2: Compute an eigenface space and project the input faces into this subspace.
Step 3: Estimate the ranked list of these faces using Rank-By-Local-Density-Score.
Step 4: Improve this ranked list using Rank-By-Bagging-ProbSVM.
Ranking by Local Density ScoreAmong the faces retrieved by text-based
search engines for a query of person-X, relevant faces usually look similar and form the largest cluster.
Ranking by Local Density Score
One approach of re-ranking these faces is to cluster based on visual similarity.
Problem
Ideal clustering results is impossible since these faces are high dimensional data and the clusters are in different shapes, sizes, and densities.
Ranking by Local Density ScoreWe use the idea of density-based clustering
described by to solve this problem.
We define the local density score (LDS) of a point p (i.e. a face) as the average distance to its k-nearest neighbors.
Ranking by Local Density Score
We do not directly use the Euclidean distance between two points in this feature space for distance(p, q).
Ranking by Local Density ScoreA high value of LDS(p, k) indicates a strong
association between p and its neighbors. Therefore, we can use this local density score to rank faces.
Ranking by Bagging of SVM Classifiers
ProblemOne limitation of the local density score
based ranking is it cannot handle faces of another person strongly associated in the k-neighbor set (for example, many duplicates).
The main idea is to use a probabilistic model to measure the relevancy of a face to person-X, P(person−X|face).
Ranking by Bagging of SVM ClassifiersImproving an input rank list by combining weak classifiers
trained from subsets annotated by that rank list.
We set p=20%
: the maximum Kendall tau distance. (set=0.05)
Ranking by Bagging of SVM Classifiers
The iterations significantly improve the final ranked list.
Experimental Results
Experimental ResultsWe performed a comparison between our
proposed method with other existing approaches.
Text Based Baseline (TBL)Distance-Based Outlier (DBO)Densest Sub-Graph based Method (DSG)Local Density Score (LDS)Unsupervised Ensemble Learning Using
Local Density Score (UEL-LDS)Supervised Learning (SVM-SUP)
Experimental Results
Performance comparison of methods.
Experimental ResultsDistribution of retrieved faces and relevant faces of 16 individuals used in experiments.
Experimental Results- Evaluation Criteria
Nret be the total number of faces returned,Nrel the number of relevant facesNhit the total number of relevant faces
Experimental Results
Conclusion
Our approach works fairly well for well known people,
where the main assumption that text-based search engines return a large fraction of relevant images is satisfied.
The aim of our future work is to study how to improve the quality of the training sets used in this iteration
(bagging SVM classifiers).