mediaeval 2016 - recod at diverse social images retrieval task

Recod @ MediaEval 2016:Diverse Social Images Retrieval

UEFS

Acknowledgments: UEFS/PROBIC, Samsung Research Institute Brazil, CNPq, and FAPESP.

Samuel G. Fadelsamuel.fadel@ic.unicamp.br

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,

o.penatti@samsung.com, jurandy.almeida@unifesp.br, [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. Fadelsamuel.fadel@ic.unicamp.br

Thank you!