The document describes the LAPI team's approach for the MediaEval 2016 predicting media interestingness task. They used a classical machine learning approach where descriptors were generated for videos and images, and support vector machines (SVMs) were trained on the development set. The best SVM-descriptor combinations were identified on the development set and used to predict the test set. Descriptors included HSV histograms, HOG, SIFT, LBP, GIST, and AlexNet layers. SVMs with linear, polynomial, and RBF kernels were tested. The best results on the development set had a MAP of 0.214 for images and 0.179 for videos. On the test set, their best run was above the estimated MAP on

1. LAPI AT MEDIAEVAL 2016 PREDICTING
MEDIA INTERESTINGNESS TASK
Mihai Gabriel Constantin, Bogdan Boteanu, Bogdan Ionescu
University Politehnica of Bucharest
LAPI - The Image Processing and Analysis Laboratory
contact: mgconstantin@alpha.imag.pub.ro
Faculty of Electronics, Telecommunications
and Information Technology
University Politehnica of Bucharest

2.  Classical machine learning approach
 Descriptors for videos and images are generated
 Support Vector Machines (SVM) are trained on devset
 Best SVM-descriptor combinations are identified
 Best combinations are used for predicting testset
OUR APPROACH
MediaEval 2016, Hilversum, Netherlands

3. MediaEval 2016, Hilversum, Netherlands
 For the video subtask we get the descriptors by
calculating the average of the individual frame
descriptors

4.  Hue-Saturation-Value Histogram
 Histogram of Oriented Gradients (HoG)
 Scale-Invariant Feature Transform (SIFT)
 Local Binary Patterns (LBP)
 GIST
 fc7 and fcprob layers of AlexNet
 Color Naming Histogram
DESCRIPTORS
MediaEval 2016, Hilversum, Netherlands

5. SVM LEARNING SYSTEMS
 Linear Kernel
 Polynomial Kernel
 Radial Basis Function (RBF) Kernel
 Weights (1/10) were used to balance the devset data
MediaEval 2016, Hilversum, Netherlands

6. EXPERIMENTS ON DEVSET
 Best results were chosen using 10-fold cross validation
 Best MAP
 Image subtask – 0.214
 Video subtask – 0.179
MediaEval 2016, Hilversum, Netherlands

7.  Only one run (run5) was above the estimated MAP on devset
 Best MAP
 Image subtask – 0.1714
 Video subtask – 0.1629
OFFICIAL RESULTS ON TESTSET
MediaEval 2016, Hilversum, Netherlands
above devset

8. THANK YOU