Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
Extraction of Shift Invariant Wavelet Features for Classification of Images with Different Sizes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture features for DCT-coded image retrieval and classification
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
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A novel approach for the classification of image signals for image retrieval using Gradient-Based Fuzzy C-Means with Mercer Kernel (GBFCM-MK) is proposed and presented in this paper. The proposed classifier is a FCM-based algorithm which utilizes the Mercer Kernel to exploit the statistical nature of the image data to improve the classification accuracy. Experiments and results on various data sets demonstrate that the proposed classification algorithm outperforms 21.7% - 24% in accuracy in comparison with conventional algorithms such as the traditional Fuzzy C-Means (FCM), Gradient-based Fuzzy C-Means (GBFCM), and GBFCM with Divergence Measure (GBFCM(DM).