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.

中文翻译：

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基于优化遗传算法的有效图像检索利用了基于SVM的卷积神经网络分类器

图像检索是从数据库检索图像的过程。某些算法已用于传统的图像检索。但是，这样的检索涉及某些限制，例如手动图像注释，无效的特征提取，无法处理复杂查询的能力，所需的时间增加以及产生的准确性较低的结果。为了克服这些问题，本研究提出了一种有效的图像检索方法。这项工作旨在使用最佳特征提取过程来有效地检索图像。在这项研究的预处理中，使用了高斯滤波技术来删除数据集中存在的多余数据。在预处理之后，将特征提取应用于提取特征，例如纹理和颜色。在这里，纹理特征被归类为灰度共生矩阵，而新颖的统计和颜色特征被认为是基于图像强度的颜色特征。这些特征通过k-均值聚类进行聚类以形成标签。改进的遗传算法用于优化特征，并使用基于SVM的新型卷积神经网络（NSVMBCNN）对这些特征进行分类。然后，根据敏感性，特异性，精确度，召回率，检索率和识别率对性能进行评估。所提出的特征提取和基于改进遗传算法的优化技术优于实验中的现有技术，并使用四个不同的数据集来测试所提出的模型。所提出方法的性能也比现有（RVM）回归矢量机DSCOP的性能好，