An effective image retrieval based on optimized genetic algorithm utilized a novel SVM-based convolutional neural network classifier

Image retrieval is the process of retrieving images from a database. Certain algorithms have been used for traditional image retrieval. However, such retrieval involves certain limitations, such as manual image annotation, ineffective feature extraction, inability capability to handle complex queries, increased time required, and production of less accurate results. To overcome these issues, an effective image retrieval method is proposed in this study. This work intends to effectively retrieve images using a best feature extraction process. In the preprocessing of this study, a Gaussian filtering technique is used to remove the unwanted data present in the dataset. After preprocessing, feature extraction is applied to extract features, such as texture and color. Here, the texture feature is categorized as a gray level cooccurrence matrix, whereas the novel statistical and color features are considered image intensity-based color features. These features are clustered by k-means clustering for label formation. A modified genetic algorithm is used to optimize the features, and these features are classified using a novel SVM-based convolutional neural network (NSVMBCNN). Then, the performance is evaluated in terms of sensitivity, specificity, precision, recall, retrieval and recognition rate. The proposed feature extraction and modified genetic algorithm-based optimization technique outperforms existing techniques in experiments, with four different datasets used to test the proposed model. The performance of the proposed method is also better than those of the existing (RVM) regression vector machine, DSCOP, as well as the local directional order pattern (LDOP) and color co-occurrence feature + bit pattern feature (CCF + BPF) methods, in terms of the precision, recall, accuracy, sensitivity and specificity of the NSVMBCNN.