Feature Annotation for Visual Concept Detection in ImageCLEF 2008

In this paper, we describe the participation of “CNRS-TELECOM Paris- Tech” at the ImageCLEF 2016 Scalable Concept Image Annotation Task [45, 46]. Our solution focuses mainly on

To limit the number of regions where a concept is likely to appear, we further merge the neighboring cells which have that concept, by retaining the largest rectangular

The aim of that task was to reduce the reliance of cleanly annotated data for concept detection and rather focus on uncovering structure from noisy data, emphasizing the importance

A novel component of our 2014 system is adaptive tag selection that determines the number of tags to be selected with respect to a given test image. Adaptive tag selection is

The logistic regression layer produces a distribution with denotes the probability of belonging to each concept, in order to give a final decision in the annotation,.. it is

The base classifiers are learned from the 250K Internet images provided by the 2013 task, while the stacked model is optimized using the development set of 1,000 ground-truthed

Then we select positive and negative examples according to different strategies to learn linear SVM models from the BoV 1 signatures.. Text model used was F lickr w 30k 0 i.e the

– UNIMORE 4 Training images are associated to a concept if the concept word is present in the stemmed scofeats file. For every image multiscale HSVSIFT, OPPONENTSIFT, RGBSIFT