A Neural Network Model for Image Change Detection Based on Fuzzy Cognitive Maps

This paper outlines a neural network model based on the Fuzzy Cognitive Maps (FCM) framework for solving the automatic image change detection problem. Each pixel in the reference image is assumed to be a node in the network. Each node has associated a fuzzy value, which determines the magnitude of the change. Each fuzzy value is updated by a trade-off between the influences received from the fuzzy values from other neurons and its own fuzzy value. Classical approaches in the literature have been designed assuming that the mutual influences between two nodes are symmetric. The main finding of this paper is the assumption that mutual influences could not be symmetric. This non symmetric relationship can be embedded by the FCM paradigm. The performance of the proposed method is illustrated by comparative analysis against some recent image change detection methods.

• Type: Conference
• Year: 2007
• Where: IBPRIA
• Authors: Gonzalo Pajares, Alfonso Sánchez-Beato, Jesús Manuel de la Cruz, José Jaime Ruz
• Keywords: Fuzzy Cognitive Maps, Fuzzy Value, IBPRIA 2007, Image, Image Analysis, Image Change Detection