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

Abstract

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 SVMbased 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.

A Study in the Early Prediction of ICT Literacy Ratings Using Sustainability in Data Mining Techniques

“…In this study, accuracy, precision, recall (sensitivity), and F1 score were used as criteria for evaluating the six data-mining algorithms [29,30]. Accuracy is the percentage of the measurement that matches the actual and predicted values of the algorithm among the total data (1).…”

Content-based image retrieval: A review of recent trends

“…On the other hand, they fail to differentiate between the background and the object in the image (different image parts). This makes them unsuitable for retrieval in complex scenes or object recognition (Halawani et al, 2006), but they are appropriate for object classification and detection (Ghrabat et al, 2019). As a comparison to global feature, local features are suitable for image retrieval, matching tasks and recognitions (Halawani et al, 2006).…”

An Efficient Retrieval System for Biomedical Images Based on Radial Associated Laguerre Moments

“…[6], introduced a CBIR system to retrieve natural and texture images. Mudhafar et al [7], proposed an effective image retrieval method based on a new support vector machine NSVM and convolution neural network CNN. BMIR systems represent one type of image retrieval systems based totally on the extraction of appropriate features (i.e.…”