Abstract
In this paper, we propose a two-phase Content-Based Retrieval System for images embedded in the Neutrosophic domain. In this first phase, we extract a set of features to represent the content of each image in the training database. In the second phase, a similarity measurement is used to determine the distance between the image under consideration (query image), and each image in the training database, using their feature vectors constructed in the first phase. Hence, the N most similar images are retrieved.
Highlights
Because of a huge growth of digital images, Content Based Image Retrieval (CBIR) is a wide research area searching for images from a large database using visual information which based on a given query image [13]
The visual features can be ordered in three levels: low level features, middle level features and high level features
There are 1000 images which contain 10 categories; every category contains 100 images of size in JPG format and compared the performance with that of some existing methods such as image retrieval system based on fuzzy sets
Summary
Because of a huge growth of digital images, Content Based Image Retrieval (CBIR) is a wide research area searching for images from a large database using visual information which based on a given query image [13]. The Content Based Image Retrieval (CBIR)[3]goalis to retrieve images relevant to a query images which selected by a user. The image in CBIR is described by extracted low-level visual features, such as color, texture and shape [9, 14, 17]. Most of recently systems were depended on low level features (color, shape). Both of mid-level and high-level image representations are in demand. The first and the main task in the CBIR systems to retrieve the similar images from database is Feature extraction. The selected similarity metrics impact on the performance of content-based image retrieval. The feature vectors selected type, determines the measurement type that used to compare their similarity. TheNeutrosophic concepts which are the degree of membership (T), Indeterminacy (I) and the degree of non-membership (F) of each element have been investigated by Salama et al [6,1922]
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