Title :
An Iterative Combination Scheme for multimodal visual feature detection
Author :
Guerra-Filho, Gutemberg
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of Texas at Arlington, Arlington, TX, USA
fDate :
Sept. 30 2012-Oct. 3 2012
Abstract :
We address the problem of multimodal visual feature detection where several individual heterogeneous measures (i.e., feature detectors) are merged into a single saliency value. A newapproach, the Iterative Combination Scheme, is proposed to iteratively learn a classifier that infers a non-linear model to combine different feature detectors. We evaluate and compare the combination strategies presented using an objective methodology, the repeatability criterion, and a dataset with real images of 21 cluttered scenes of 3D objects.
Keywords :
feature extraction; image classification; iterative methods; learning (artificial intelligence); object detection; 3D object; classifier learning; iterative combination scheme; multimodal visual feature detection; repeatability criterion; saliency value; Detectors; Feature extraction; Iterative methods; Laplace equations; Training; Vectors; Visualization; iterative combination scheme; multimodal detection; visual feature;
Conference_Titel :
Image Processing (ICIP), 2012 19th IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4673-2534-9
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2012.6466806