Elements of information theory
Elements of information theory
International Journal of Computer Vision
Performance evaluation in content-based image retrieval: overview and proposals
Pattern Recognition Letters - Special issue on image/video indexing and retrieval
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Similarity between Euclidean and cosine angle distance for nearest neighbor queries
Proceedings of the 2004 ACM symposium on Applied computing
Content-based image classification using a neural network
Pattern Recognition Letters
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Dependency and Correlation Analysis for Features
IEEE Transactions on Knowledge and Data Engineering
Increasing the discrimination power of the co-occurrence matrix-based features
Pattern Recognition
Rotation-invariant and scale-invariant Gabor features for texture image retrieval
Image and Vision Computing
Computer Methods and Programs in Biomedicine
A smart content-based image retrieval system based on color and texture feature
Image and Vision Computing
A fusion neural network classifier for image classification
Pattern Recognition Letters
Average-case analysis of a nearest neighbor algorthim
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 2
Measuring effectiveness of a dynamic artificial neural network algorithm for classification problems
Expert Systems with Applications: An International Journal
A novel image retrieval model based on the most relevant features
Knowledge-Based Systems
Fast color-spatial feature based image retrieval methods
Expert Systems with Applications: An International Journal
IEEE Transactions on Multimedia
Content-based image retrieval using color and texture fused features
Mathematical and Computer Modelling: An International Journal
Color image indexing using BTC
IEEE Transactions on Image Processing
Similarity-based online feature selection in content-based image retrieval
IEEE Transactions on Image Processing
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Adopting effective model to access the desired images is essential nowadays with the presence of a huge amount of digital images. The present paper introduces an accurate and rapid model for content based image retrieval process depending on a new matching strategy. The proposed model is composed of four major phases namely: features extraction, dimensionality reduction, ANN classifier and matching strategy. As for the feature extraction phase, it extracts a color and texture features, respectively, called color co-occurrence matrix (CCM) and difference between pixels of scan pattern (DBPSP). However, integrating multiple features can overcome the problems of single feature, but the system works slowly mainly because of the high dimensionality of the feature space. Therefore, the dimensionality reduction technique selects the effective features that jointly have the largest dependency on the target class and minimal redundancy among themselves. Consequently, these features reduce the calculation work and the computation time in the retrieval process. The artificial neural network (ANN) in our proposed model serves as a classifier so that the selected features of query image are the input and its output is one of the multi classes that have the largest similarity to the query image. In addition, the proposed model presents an effective feature matching strategy that depends on the idea of the minimum area between two vectors to compute the similarity value between a query image and the images in the determined class. Finally, the results presented in this paper demonstrate that the proposed model provides accurate retrieval results and achieve improvement in performance with significantly less computation time compared with other models.