Feature selection for object detection: the best group vs. the group of best

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

Colleges, School and Institutes

External organisations

  • Faculty of Computer and Information Science, University of Ljubljana

Abstract

The problem of visual object detection, the goal of which is to predict the locations and sizes of all objects of a given visual category (e.g., cars) in a given set of images, is often based on a possibly large set of local features, only a few of which might actually be useful for the given detection setup. Feature selection is concerned with finding a 'useful' subset of features. In this paper, we compare two approaches to feature selection in a visual object detection setup. One of them selects features based on their individual utility scores alone, regardless of possible interdependence with other features. The other approach employs the AdaBoost framework and hence implicitly deals with interdependence. Using two feature extraction methods and several image datasets, we experimentally confirm the significance of feature interdependence: features that perform well individually do not necessarily perform well as a group.

Details

Original languageEnglish
Title of host publication2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2014 - Proceedings
Publication statusPublished - 26 May 2014
Event2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2014 - Opatija, Croatia
Duration: 26 May 201430 May 2014

Publication series

NameInternational Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)
Volume2014

Conference

Conference2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2014
CountryCroatia
CityOpatija
Period26/05/1430/05/14

Keywords

  • Feature extraction, Training, Object detection, Visualization, Image segmentation, Detection algorithms, Educational institutions