Fusion of remote sensing data from multiple sensors has been remarkably increased for classification. This is because, additional sources may provide more information, and fusion of different information can produce a better understanding of the observed site. In the field of data fusion, fusion of light detection and ranging (LIDAR) and optical remote sensing data for land cover classification...

Recently, the necessity for content-based music retrieval that can return results even if a user does not know information such as the title or singer has increased. Query-by-humming (QBH) systems have been introduced to address this need, as they allow the user to simply hum snatches of the tune to find the right song. Even though there have been many studies on QBH, few have combined multiple...

Most the objective of intelligent systems is to create a model, which given a minimum amount of input data or information, is able to produce reliable recognition rates and correct decisions. In the application, when an individual classifier has reached its limit and, at the same time, it is hard to develop a better one, the solution might only be to combine the existing well performing classif...

ion In this paper, we describe our first participation for the semantic indexing task at TRECVID 2010 [1]. We focus on extraction multiple low-level feature sets and a fusion method. In our system, six features are extracted for all the predefined concepts from the keyframes, including global features (RGB color histogram, HSV color histogram, edge histogram, Grey Level Co-occurrence Matrix, GI...

The present article proposes a new neuro-fuzzy-fusion (NFF) method for combining the output of a set of fuzzy classifiers in a multiple classifier system (MCS) framework. In the proposed method the output of a set of classifiers (i.e., fuzzy class labels) are fed as input to a neural network, which performs the fusion task. The proposed fusion technique is tested on a set of remote sensing imag...

A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid classifiers are used to improve discriminatory power. Traditional fusion rules use the product of multi-single classifiers, but that strategy cannot integrate the classification output of machine learning classifiers. In this research, the performance of two hybrid strategies...