Finding Diverse Social Images
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This document summarizes a team's participation in the MediaEval 2015 Workshop on Retrieving Diverse Social Images. Their approach used a supervised maximal marginal relevance method (sMMR) to jointly optimize relevance and diversity when retrieving images. They tested sMMR using different combinations of visual and textual features. sMMR builds a refined result set incrementally by selecting images that score highest based on relevance to the query and diversity from images already selected. The team trained relevance classifiers using ground truth image labels for queries. Their work was supported by the USEMP project and they provided more details in a poster session.
![Summary of our participation
• supervised Maximal Marginal Relevance (sMMR) [1]:
– A supervised diversification method that jointly optimizes
relevance and diversity
• The runs
– Fully automated, no external data*
– Each run corresponds to a different instantiation of sMMR
#2
Run id Run Type Relevance Features Diversity Features
1 visual-only CNN* [1] VLAD+CSURF [2]
2 text-only BOW BOW
3 & 5 visual+textual CNN, BOW, META VLAD+CSURF
[1] E. Spyromitros-Xioufis et al., “Improving diversity in image search via supervised relevance scoring”, ICMR 2015
[2] E. Spyromitros-Xioufis et al., “A comprehensive study over VLAD and Product Quantization in large-scale image
retrieval”, IEEE Transactions on Multimedia, 2014](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Finding Diverse Social Images
- 1. MediaEval 2015 Workshop, Retrieving Diverse Social Images Task 14-15 September 2015, Wurzen, Germany USEMP: Finding Diverse Images at MediaEval 2015 Eleftherios Spyromitros-Xioufis1, Adrian Popescu2, Symeon Papadopoulos1, Yiannis Kompatsiaris1 1 CERTH-ITI, Thermi-Thessaloniki, Greece, {espyromi,papadop,ikom}@iti.gr 2 CEA, LIST, 91190 Gif-sur-Yvette, France, adrian.popescu@cea.fr
- 2. Summary of our participation • supervised Maximal Marginal Relevance (sMMR) [1]: – A supervised diversification method that jointly optimizes relevance and diversity • The runs – Fully automated, no external data* – Each run corresponds to a different instantiation of sMMR #2 Run id Run Type Relevance Features Diversity Features 1 visual-only CNN* [1] VLAD+CSURF [2] 2 text-only BOW BOW 3 & 5 visual+textual CNN, BOW, META VLAD+CSURF [1] E. Spyromitros-Xioufis et al., “Improving diversity in image search via supervised relevance scoring”, ICMR 2015 [2] E. Spyromitros-Xioufis et al., “A comprehensive study over VLAD and Product Quantization in large-scale image retrieval”, IEEE Transactions on Multimedia, 2014
- 3. Overview of our approach • sMMR builds incrementally a refined set 𝑆 ⊂ 𝐼, 𝑆 = 𝐾 • At each step 𝐽 = 1, … , 𝐾 selects the image 𝑖𝑚∗ that scores highest to the following criterion: #3 𝑈(𝑖𝑚∗ |𝑞) = 𝑤 ∗ 𝑅 𝑖𝑚∗ 𝑞 + 1 − 𝑤 ∗ min 𝑖𝑚 𝑗∈𝑆 𝐽−1 𝑑(𝑖𝑚∗ , 𝑖𝑚𝑗)
- 4. Overview of our approach • sMMR builds incrementally a refined set 𝑆 ⊂ 𝐼, 𝑆 = 𝐾 • At each step 𝐽 = 1, … , 𝐾 selects the image 𝑖𝑚∗ that scores highest to the following criterion: #4 𝑈(𝑖𝑚∗ |𝑞) = 𝑤 ∗ 𝑅 𝑖𝑚∗ 𝑞 + 1 − 𝑤 ∗ min 𝑖𝑚 𝑗∈𝑆 𝐽−1 𝑑(𝑖𝑚∗ , 𝑖𝑚𝑗) Relevance to the query output of a task and query specific classifier
- 5. Overview of our approach • sMMR builds incrementally a refined set 𝑆 ⊂ 𝐼, 𝑆 = 𝐾 • At each step 𝐽 = 1, … , 𝐾 selects the image 𝑖𝑚∗ that scores highest to the following criterion: #5 𝑈(𝑖𝑚∗ |𝑞) = 𝑤 ∗ 𝑅 𝑖𝑚∗ 𝑞 + 1 − 𝑤 ∗ min 𝑖𝑚 𝑗∈𝑆 𝐽−1 𝑑(𝑖𝑚∗ , 𝑖𝑚𝑗) Relevance to the query output of a task and query specific classifier Diversity in 𝑆 distance to the most similar image already selected
- 6. Learning relevance from ground truth #6 devset queries q1 q2 q3 test query, e.g. “Eiffel Tower” Wikipedia images Flickr images ? ? ? ? ? Flickrimages
- 7. Learning relevance from ground truth #7 devset queries q1 q2 q3 test query, e.g. “Eiffel Tower” Wikipedia images Flickr images ? ? ? ? ? Flickrimages training set for ℎeiffel
- 8. Learning relevance from ground truth #8 devset queries q1 q2 q3 test query, e.g. “Eiffel Tower” Wikipedia images Flickr images ? ? ? ? ? Flickrimages training set for ℎeiffel
- 9. Learning relevance from ground truth #9 devset queries q1 q2 q3 test query, e.g. “Eiffel Tower” Wikipedia images Flickr images ? ? ? ? ? Flickrimages training set for ℎeiffel
- 10. Learning relevance from ground truth #10 devset queries q1 q2 q3 test query, e.g. “Eiffel Tower” Wikipedia images Flickr images ? ? ? ? ? Flickrimages training set for ℎeiffel
- 11. Learning relevance from ground truth #11 devset queries q1 q2 q3 test query, e.g. “Eiffel Tower” Wikipedia images Flickr images ? ? ? ? ? Flickrimages training set for ℎeiffel
- 12. #12 This work was supported by the USEMP FP7 project More details at the poster session!
Notes de l'éditeur
- Here is an overview of our approach. sMMT builds a refined set of images S with K elements from a larger set of images I, incrementally at K steps. At each step, the method greedily selects to include in S the image (among the unselected ones) that maximizes the following criterion that jointly considers relevance and diversity. The criterion is a weighted combination of a Relevance score and a Diversity score: The relevance score is basically the output of a classifier that is trained to distinguish relevant from irrelevant images. It is task specific because it uses the relevance ground truth given for this task and query specific because it includes the Wikipedia images/page given for each location in the set of positive/relevant examples. For the diversity part, we define the diversity score for an image at step J, as being equal to the distance of this images to the most similar image among those already included in S.
- Here is an overview of our approach. sMMT builds a refined set of images S with K elements from a larger set of images I, incrementally at K steps. At each step, the method greedily selects to include in S the image (among the unselected ones) that maximizes the following criterion that jointly considers relevance and diversity. The criterion is a weighted combination of a Relevance score and a Diversity score: The relevance score is basically the output of a classifier that is trained to distinguish relevant from irrelevant images. It is task specific because it uses the relevance ground truth given for this task and query specific because it includes the Wikipedia images/page given for each location in the set of positive/relevant examples. For the diversity part, we define the diversity score for an image at step J, as being equal to the distance of this images to the most similar image among those already included in S.
- Here is an overview of our approach. sMMT builds a refined set of images S with K elements from a larger set of images I, incrementally at K steps. At each step, the method greedily selects to include in S the image (among the unselected ones) that maximizes the following criterion that jointly considers relevance and diversity. The criterion is a weighted combination of a Relevance score and a Diversity score: The relevance score is basically the output of a classifier that is trained to distinguish relevant from irrelevant images. It is task specific because it uses the relevance ground truth given for this task and query specific because it includes the Wikipedia images/page given for each location in the set of positive/relevant examples. For the diversity part, we define the diversity score for an image at step J, as being equal to the distance of this images to the most similar image among those already included in S.