"Semantic Indexing of Wearable Camera Images: Kids’Cam Concepts"
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Slides from Workshop on Vision and Language Integration Meets Multimedia Fusion at ACM MULTIMEDIA Conference, Amsterdam, August 2016
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"Semantic Indexing of Wearable Camera Images: Kids’Cam Concepts"
- 1. Semantic Indexing of Wearable Camera Images: Kids’Cam Concepts Alan F. Smeaton (Dublin City University) … and …
- 2. ... Kevin McGuinness and Cathal Gurrin and Jiang Zhou and Noel E. O’Connor and Peng Wang and Brian Davis and Lucas Azevedo and Andre Freitas and Louise Signal and Moira Smith and James Stanley and Michelle Barr and Tim Chambers and Cliona Ní Mhurchu
- 3. Overview • Automatic assignment of one-per-class concept detectors is now commonplace. • We’re interested in the challenging case of processing images from wearable cameras where improvement is necessary. • We try to exploit some limited manual annotations to improve accuracy of automatic concept weights. • This work is not complete, its ongoing, but the story is interesting.
- 4. Analysis of Visual Media • More progress made within the last few years than in previous decade • Incorporation of deep learning plus availability of huge searchable image resources and training data • Automatic image tagging is now hosted and offered by website like Aylien, Imagga, Clarifai, and others, and very cost-effective.
- 5. Analysis of Visual Media • These developments are welcome … but … restrictive tagging vocabularies. • How to map to users formulating queries • Alternative approach is tagging at query time but its expensive and not scalable to huge collections. • Almost all work on concept detection based on one concept at a time. • TRECVid tried simultaneous detection of concept pairs like “computer- screen with telephone”, and “airplane with clouds”. • Limited success but “Government Leader with Flag” was OK ! • Detection of concepts independently needs a course-correction because: – Doesn’t avail of all available information sources – Doesn’t map to a user’s search vocabulary
- 6. Long-term approach … Images Concept Set Mapping User Search vocabulary How can a single image be mapped to two different vocabularies ?
- 7. Using NL for image search … tagging • NL is fraught with complexities, ambiguities at all levels .. – Lexical level polysemy – Syntactic level structural ambiguity – Semantic interpretations – Discourse level pronoun resolution • + vocabulary limitations when finding word or phrase to describe something • When using computers to help search for image data, language challenges are exacerbated yet we assume a “simplistic” approach of tagging by a set of concepts, notwithstanding what we’re seeing with captioning here today • Tagging is very useful for smaller, niche applications in restricted domains with manual tagging, but we see scalability problems – Addressed with progress in automatic tagging but we’re tolerant of inaccuracies !
- 8. In this paper … • We are interested in images from wearable cameras with lots of juicy challenges. • Notoriously difficult to process automatically because … – Blurring caused by wearer moving at image capture – Occlusions from wearer’s hands – Lighting conditions – Fisheye lens for wider perspective causing distortion – First person viewpoint but not what wearer sees – Content varies hugely across subjects • Applications in memory support, behaviour recording and analysis, security, other work-related, and QS. • In this paper we work with wearable camera data from school children, for analysis of their environments
- 10. The Kids’Cam Project • Child obesity is a significant public health concern, worldwide. • Unequivocal evidence that marketing of energy-dense and nutrient- poor foods and beverages is a causal factor in child obesity. • Evidence of children’s total exposure to advertising of poor foodstuffs is not quantified. • Kids’Cam study aimed to determine the frequency, nature and duration of children’s exposure to such marketing. • 169 randomly selected children 11 to 13 yo from 16 schools in Wellington, NZ, each wore an Autographer and carried a GPS for 4 days .. .mages every 7 seconds, GPS every 5 seconds. – 1.5M images, 2.5M GPS datapoints • Manual annotation for food / beverage marketing using a 3-level, 53 concept ontology .. Inter-annotator reliability of 90%.
- 11. + GPS + Date/Time + User xxxx yyyy zzzz xxxx yyyy zzzz xxxx yyyy zzzz xxxx yyyy zzzz 85 concepts Manual Annotation
- 12. Shop front > sign > sugary drinks/juices
- 13. Convenience store indoors > in-store marketing > convenience store
- 14. School > sign > fast food
- 15. Processing the Kids’Cam Data • Following integration of different data sources and after the manual annotation of images, we processed the image collection in the following way …
- 16. 14 + GPS + Date/Time + User xxxx yyyy zzzz xxxx yyyy zzzz xxxx yyyy zzzz xxxx yyyy zzzz 85 concepts : X 1.5M images aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc TFR aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ GPU Concept Models 6,000 concepts xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx aaaaa bbbb ccccc aaaa bbbb ccccc aaaa bbbb ccccc aaaa bbbb ccccc 8 7 6 5 4 3 2 1 9 13 12 11 10
- 17. 14 + GPS + Date/Time + User xxxx yyyy zzzz xxxx yyyy zzzz xxxx yyyy zzzz xxxx yyyy zzzz 85 concepts : X 1.5M images aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc TFR aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ GPU Concept Models 6,000 concepts xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx aaaaa bbbb ccccc aaaa bbbb ccccc aaaa bbbb ccccc aaaa bbbb ccccc 8 7 6 5 4 3 2 1 9 13 12 11 10
- 18. 14 + GPS + Date/Time + User xxxx yyyy zzzz xxxx yyyy zzzz xxxx yyyy zzzz xxxx yyyy zzzz 85 concepts : X 1.5M images aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc TFR aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ GPU Concept Models 6,000 concepts xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx aaaaa bbbb ccccc aaaa bbbb ccccc aaaa bbbb ccccc aaaa bbbb ccccc 8 7 6 5 4 3 2 1 9 13 12 11 10 7. Using a CNN to apply tags to images. We used the VGG-16 network, a deep CNN, trained on 1,000 object classes using 1.2M images from ImageNet
- 19. 14 + GPS + Date/Time + User xxxx yyyy zzzz xxxx yyyy zzzz xxxx yyyy zzzz xxxx yyyy zzzz 85 concepts : X 1.5M images aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc TFR aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ GPU Concept Models 6,000 concepts xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx aaaaa bbbb ccccc aaaa bbbb ccccc aaaa bbbb ccccc aaaa bbbb ccccc 8 7 6 5 4 3 2 1 9 13 12 11 10 8. Trained models were used to predict probabilities for each concept in each of 1.5M images. Processed in batches of 64 on NVIDIA GPU, taking 4 days to complete
- 20. Training Free Refinement • Current concept-at-a-time classifiers do not consider inter- concept relationships or dependencies yet these do exist • To improve one-per-class detectors, we post-process detection scores – We take advantage of concept co-occurrence and re- occurrence which depend on the particular collection – We take advantage of local (temporal) neighbourhood information where concepts are likely to re-occur close in time – We use GPS location information where concepts identified by a person at a location, may re-occur subsequently at that same location • TFR is based on non-negative matrix factorisation, described elsewhere
- 21. 14 + GPS + Date/Time + User xxxx yyyy zzzz xxxx yyyy zzzz xxxx yyyy zzzz xxxx yyyy zzzz 85 concepts : X 1.5M images aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc aaaa bbbb cccc TFR aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ aaaa’ bbbb’ cccc’ GPU Concept Models 6,000 concepts xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx xxxx xxxxx xxxxx aaaaa bbbb ccccc aaaa bbbb ccccc aaaa bbbb ccccc aaaa bbbb ccccc 8 7 6 5 4 3 2 1 9 13 12 11 10 9. Previously-described, we then applied Training-Free Refinement to improve probability assignments
- 22. • We do not know accuracy of assignment of 1,000 concepts but we know accuracy of assignment of 53 concepts …and we have 1.5M images each mapped into 2 concept spaces • Can we adjust values in (b), anchored and pivoting around (a) in addition to having already used local, within-collection distributions ? y1 y2 x1 x2 b2 b1 a1 a2 (a) Manual, correct (b) Automatic, unknown accuracy
- 23. y1 y2 x1 x2 b2 b1 a1 a2 (a) (b) b2’ b1’ a1’ a2’ (c)
- 24. Cross-mapping concept spaces • Distributional semantics – corpus-driven approach – based on hypothesis that co-occurring words in similar contexts have similar meaning • Using word2vec in DINFRA, we can map all words in a vocabulary to an n-dimensional vector space, where we can obtain relatedless scores among the words • Figure illustrates an example • For each image in Kids’Cam we can evaluate relatedness between human annotation and automatic concepts with highest-probability
- 25. School > availability > drink bottle
- 26. • We have top-ranked concepts, their confidences, their relatedness to the manual tags … • First effort is to simply multiple, as in Table, but its hard to see the impact of this
- 27. And the result is … • … and that’s where we currently are !
- 28. Conclusions and Future Work • Since automatic concept-detection using pre-defined models has made so much progress recently, we’re seeing vocabulary / concept space mis-matching • Using 1.5M Kids’Cam images from wearable cameras, we have used within-collection distributions to “smooth” concept weights (outliers and gaps) in TFR • We are trying to pivot around some manual annotations in order to improve concept accuracies • But, we need … – More concepts – a richer vocabulary of them – More varied manual annotations, not just fast food adverts – A more global or collection-wide way to combine concept confidences and relatedless to known manual annotations – Some validation of accuracy of automatic concepts to measure accuracy of our post-processing
- 29. Finally, a plug … • TRECVid Video captioning Pilot task 2016 • 2,000 x Vine Videos, manually annotated with captions, twice • 8 participating groups (CMU, CUHK, DCU, GMU, NII, UvA, Sheffield) • Two tasks … – For each video, rank the 2,000 captions – metric is MRR – For each video, generate your own caption – metrics are bleu, meteor, and UMBC STS (Semantic Textual Similarity) Service • Lots of lessons learned and will build upon for full task in 2017, probably using Vine videos