Learning imprecise semantic concepts from image databases.

Daniel Sánchez; Jesús Chamorro-Martínez

Mathware and Soft Computing (2002)

  • Volume: 9, Issue: 1, page 59-73
  • ISSN: 1134-5632

Abstract

top
In this paper we introduce a model to represent high-level semantic concepts that can be perceived in images. The concepts are learned and represented by means of a set of association rules that relate the presence of perceptual features to the fulfillment of a concept for a set of images. Since both the set of images where a perceptual feature appears and the set of images fulfilling a given concept are fuzzy, we use in fact fuzzy association rules for the learning model. The concepts so acquired are useful in several applications, in particular they provide a new way to formulate imprecise queries in image databases.

How to cite

top

Sánchez, Daniel, and Chamorro-Martínez, Jesús. "Learning imprecise semantic concepts from image databases.." Mathware and Soft Computing 9.1 (2002): 59-73. <http://eudml.org/doc/39234>.

@article{Sánchez2002,
abstract = {In this paper we introduce a model to represent high-level semantic concepts that can be perceived in images. The concepts are learned and represented by means of a set of association rules that relate the presence of perceptual features to the fulfillment of a concept for a set of images. Since both the set of images where a perceptual feature appears and the set of images fulfilling a given concept are fuzzy, we use in fact fuzzy association rules for the learning model. The concepts so acquired are useful in several applications, in particular they provide a new way to formulate imprecise queries in image databases.},
author = {Sánchez, Daniel, Chamorro-Martínez, Jesús},
journal = {Mathware and Soft Computing},
keywords = {Lógica difusa; Análisis digital de imágenes; Recuperación de información; image semantics; fuzzy association rules; image databases},
language = {eng},
number = {1},
pages = {59-73},
title = {Learning imprecise semantic concepts from image databases.},
url = {http://eudml.org/doc/39234},
volume = {9},
year = {2002},
}

TY - JOUR
AU - Sánchez, Daniel
AU - Chamorro-Martínez, Jesús
TI - Learning imprecise semantic concepts from image databases.
JO - Mathware and Soft Computing
PY - 2002
VL - 9
IS - 1
SP - 59
EP - 73
AB - In this paper we introduce a model to represent high-level semantic concepts that can be perceived in images. The concepts are learned and represented by means of a set of association rules that relate the presence of perceptual features to the fulfillment of a concept for a set of images. Since both the set of images where a perceptual feature appears and the set of images fulfilling a given concept are fuzzy, we use in fact fuzzy association rules for the learning model. The concepts so acquired are useful in several applications, in particular they provide a new way to formulate imprecise queries in image databases.
LA - eng
KW - Lógica difusa; Análisis digital de imágenes; Recuperación de información; image semantics; fuzzy association rules; image databases
UR - http://eudml.org/doc/39234
ER -

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

                
Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.