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Beauty is Here! Evaluating Aesthetics in Videos Using Multimodal Features and Free Training Data

Beauty is Here! Evaluating Aesthetics in Videos Using Multimodal Features and Free Training Data. Yanran Wang, Qi Dai, Rui Feng, Yu-Gang Jiang. School of Computer Science, Fudan University, Shanghai, China. ACM MM, Barcelona, Catalunya, Spain, 2013. Overview. Task:

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Beauty is Here! Evaluating Aesthetics in Videos Using Multimodal Features and Free Training Data

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  1. Beauty is Here!Evaluating Aesthetics in Videos UsingMultimodal Features and Free Training Data Yanran Wang, Qi Dai, Rui Feng, Yu-Gang Jiang School of Computer Science, Fudan University, Shanghai, China ACM MM, Barcelona, Catalunya, Spain, 2013

  2. Overview • Task: • Designasystem to automatically identify aesthetically more appealing videos • Contribution: • Proposetousefree trainingdata • Useandevaluate various kinds of features • Result： • Attain aSpearman‘s rank correlation coefficientof0.41 on theNHK Dataset

  3. Free Training Data • Construct two annotation-free training datasets by assuming images/videos on certain websites are mostly beautiful + DPChallenge images + Flickr videos - Dutch documentary videos

  4. Free Training Data • The first training set • Using images from DPChallenge as positivesamples, • andtheDutch documentaryvideos frames as negativesamples • The second training set • Using videos from Flickr as positivesamples, • andtheDutch documentary videos as negativesamples

  5. MultimodalFeatures Color LBP SIFT HOG Classemes [ECCV’10] Traditional VisualFeatures Mid-level Semantic Attributes Style Descriptor Video Motion Feature Dense Trajectory [CVPR’11]

  6. Framework Ranking List Classifiers Feature Extraction SVM Models (Image Training Data) Image Low-Level Features (Color, LBP, SIFT, HOG) Input Videos Mid-Level Semantic Attributes (Classemes) … SVM Models (Video Training Data) Style Descriptor Video Motion Feature (Dense Trajectory)

  7. Result • Using training datafrom Flickr & Dutch Documentary videos • Evaluatedonasubsetlabeledbyourselves Dense Trajectory which is very powerful in human action recognition, performs poorly,indicatingthatmotionislessrelatedtobeauty Thebestsinglefeature Spearman's rank correlation

  8. Result • Using training data fromDPChallenge & Dutch Documentary images/frames • Evaluatedonasubsetlabeledbyourselves Thebestresult Thebestsinglefeature 0.43 0.41 Spearman's rank correlation Image-based training is more suitable on NHK dataset,becausemostNHKvideosfocusonscenes.

  9. Result • OfficialevaluationresultsfromNHK,ontheentiretestset • Wesubmitted5runs • EvaluatedonNHK’sofficiallabels,whicharenotpubliclyavailable • Observations • Imagetrainingdataismoreeffective,similartoobservationsonthesmallsubset • ColorandClassemesarecomplementary,SIFTisnot • NOTE:Thesesubmittedrunswereselectedbeforeannotatingthesubset,whichwasdonelatertoprovidemoreinsightsinthepaper!

  10. Demo Acollectionof clipsfromthetop10videosidentifiedbyoursystem

  11. Thank you!

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