Download - SheepDog – Group and Tag Recommendation for Flickr Photos by Automatic Search-based Learning
SheepDog – Group and Tag Recommendation for Flickr Photos by Automatic Search-based Learning
IntroductionPeople who use album on sharing websites always like their photos to be:
Popular : more people view their photos & good appreciation Easy management : while attach to groups & add tags are big trouble(about 15000 groups related to “dog” on Flickr)Our Goal:recommend photo(s) to suitable and popular groups and attach relevant tags to each photo automatically
Prediction result ●
Photo-level data collection
Group-level data collection
(b) Training Data Acquisition
Feature extraction
(d) Concept Detection
SVM training
(c) Model Learning
Flickr photo Database
concept detector
Feature extraction
top-n concepts
c1 cn…
g1,1 g1,p… gn,pgn,1 …
(e) Group Recommendation
(f) Tag Recommendation
Input test image
…T1,1
T1,2
T1,d
… Tn,1
Tn,2
T n,d
…
Recommend to user
Recommend to user
(a) Concept Definition -- “dog”, “tiger”, “flower”, …
Concept DetectionCompare two source of pseudo-positive training data from Flickr Photo-Level data mechanism Group-Level data mechanismProvide a new idea of “how to acquire reliable search-based data ”
The SVM predictor gives each concept a probability value to indicate the degree that the input photo fits this concept. For the top-n concepts, we recommend users the most popular groups and tags related to these concepts using our ranking algorithm.
Animal ArchitectureNature Scene Portrait Plant
Color Oriented
Overall Average
Photo level 1.10 1.51 1.72 1.07 1.79 1.51 1.55
Group level 1.24 1.68 1.90 1.40 1.92 1.56 1.69
tiger cat animals dog monkey snake rabbit portrait bird horse0%
4%
8%
12%
16%
20%Photo-level probability distribution average
tiger animals dog cat snake monkey portrait rabbit bird wood0%
4%
8%
12%
16%
20%
Group-level probability distribution average
Subjective test result The score for the top-3 concepts recommendation results [1]: S = (P*1 + R*0.5 +W*0)/Nc (Nc = 15)
[1] L. S. Kennedy, S.-F. Chang, and I. V. Kozintsev, "To search or to label?: predicting the performance of search-based automatic image classifiers," in Proc. ACM MIR’06, pp.249 – 258, 2006.
Hong-Ming Chen, Ming-Hsiu Chang, Ping-Chieh Chang, Ming-Chun Tien, Winston H. Hsu, and Ja-Ling WuCommunications and Multimedia Lab, National Taiwan University{blacksmith,cmhsiu,pingchieh,trimy,winston,wjl}@cmlab.csie.ntu.edu.tw