Semantic-Analysis Object Recognition: Automatic Training Set Generation Using Textual Tags

Training sets of images for object recognition are the pillars on which classifiers base their performances. We have built a framework to support the entire process of image and textual retrieval from search engines, which, giving an input keyword, performs a statistical and a semantic analysis and automatically builds a training set. We have focused our attention on textual information and we have explored, with several experiments, three different approaches to automatically discriminate between positive and negative images: keyword position, tag frequency and semantic analysis. We present the best results for each approach.

Tipo Pubblicazione: 
Contributo in atti di convegno
Author or Creator: 
Abdulhak, Sami Abduljalil
Riviera, Walter
Zeni, Nicola
Cristani, Matteo
Ferrario, Roberta
Cristani, Marco
Publisher: 
Springer, Berlin , Germania
Source: 
Computer Vision - ECCV Workshops, pp. 309–322, Zurich, 07/09/2014
Date: 
2015
Resource Identifier: 
http://www.cnr.it/prodotto/i/342946
https://dx.doi.org/10.1007/978-3-319-16181-5_22
info:doi:10.1007/978-3-319-16181-5_22
urn:isbn:978-3-319-16180-8
Language: 
Eng
ISTC Author: 
Ritratto di Roberta Ferrario
Real name: