Content-based Image Retrieval with Color and Texture Features in Neutrosophic Domain

In this paper, a new content-based image retrieval (CBIR) scheme is proposed in neutrosophic (NS) domain. For this task, RGB images are first transformed to three subsets in NS domain and then segmented. For each segment of an image, color features including dominant color discribtor (DCD), histogram and statistic components are extracted. Wavelet features are also extracted as texture features from the whole image. All extracted features from either segmented image or the whole image are combined to create a feature vector. Feature vectors are presented for ant colony optimization (ACO) feature selection which selects the most relevant features. Selected features are used for final retrieval process. Proposed CBIR scheme is evaluated on Corel image dataset. Experimental results show that the proposed method outperforms our prior method (with the same feature vector and feature selection method) by 2% and 1% with respect to precision and recall, respectively. Also, the proposed method achieves the improvement of 13% and 2% in precision and recall, respectively, in comparison with prior methods.