On the use of simple geometric descriptors provided by RGB-D sensors for re-identification

[EN]The re-identification problem has been commonly accomplished using appearance features based on salient points and color information. In this paper, we focus on the possibilities that simple geometric features obtained from depth images captured with RGB-D cameras may offer for the task, particularly working under severe illumination conditions. The results achieved for different sets of simple geometric features extracted in a top-view setup seem to provide useful descriptors for the re-identification task, which can be integrated in an ambient intelligent environment as part of a sensor network.

Evaluation of LBP and HOG descriptors for clothing attribute description

[EN]In this work an experimental study about the capability of the LBP, HOG descriptors and color for clothing attribute classification is presented. Two different variants of the LBP descriptor are considered, the original LBP and the uniform LBP. Two classifiers, Linear SVM and Random Forest, have been included in the comparison because they have been frequently used in clothing attributes classification. The experiments are carried out with a public available dataset, the clothing attribute dataset, that has 26 attributes in total. The obtained accuracies are over 75% in most cases, reaching 80% for the necktie or sleeve length attributes.

Exploring the use of local descriptors for fish recognition in LifeCLEF 2015

[EN]This paper summarizes the proposal made by the SIANI team for the LifeCLEF 2015 Fish task. The approach makes use of standard detection techniques, applying a multiclass SVM based classifier on large enough Regions Of Interest (ROIs) automatically extracted from the provided video frames. The selection of the detection and classification modules is based on the best performance achieved for the validation dataset consisting of 20 annotated videos. For that dataset, the best classification achieved for an ideal detection module, reaches an accuracy around 40%.