mediaeval 2016 - recod at diverse social images retrieval task
TRANSCRIPT
Recod @ MediaEval 2016:Diverse Social Images Retrieval
UEFS
Acknowledgments: UEFS/PROBIC, Samsung Research Institute Brazil, CNPq, and FAPESP.
Samuel G. [email protected]
Cristiano D. Ferreira, Rodrigo T. Calumby, Iago B. A. do C. Araujo,Ícaro C. Dourado, Javier A. V. Munoz, Otávio A. B. Penatti,
Jurandy AlmeidaLin T. Li, Ricardo da S. Torres
[rtcalumby,ibacaraujo]@ecomp.uefs.br, [crferreira,icarocd,jalvarm.acm]@gmail.com,
[email protected], [email protected], [lintzyli,rtorres]@ic.unicamp.br
PROPOSED APPROACH
Textualinput
output
Clustering
Selection
Re-ranking
Diversification
Genetic programming
Fusion
Original
Re-ranked
Re-ranked lists GP trained individual
Aggregationfunction
Fusedrank
Clustering
Representativeselection
Final Rank
RESULTS – OFFICIAL MEASURES
Run Re-ranking GP Fusion Diversification P@20 CR@20 F1@20
1 - No Agglomerative (ColorLayout) 0.5180 0.4001 0.4258
2 Cosine No Birch (Cosine) 0.5000 0.3709 0.4045
3 Cosine No Birch (ScalableColor) 0.3881 0.3881 0.4107
4 All Yes Agglomerative (ColorLayout) 0.5156 0.4173 0.4339
5 All Yes Agglomerative (ColorLayout) 0.5156 0.4065 0.4379
CONCLUSIONS
- GP fusion not always provided best results
- Cosine measure present in many GP individuals
- Use of both visual and textual information improved results
Recod @ MediaEval 2016:Diverse Social Images Retrieval
UEFS
Acknowledgments: UEFS/PROBIC, Samsung Research Institute Brazil, CNPq, and FAPESP.
Samuel G. [email protected]
Thank you!