In recent years, we have witnessed a massive growth in the generation of images on the cyberspace which demands to develop automated solutions for effective content management. Content-based image retrieval (CBIR) systems have been proposed to reduce the dependency on textual annotations-based image retrieval systems. There exists a variety of features-classifier combinations based CBIR methods to analyze the content of query image for relevant images retrieval. Although, these methods provide better retrieval performance in single-class scenario, however, we experience a significant performance drop in multi-class search environments due to semantics similarity among the images of different classes. CBIR methods based on the hybrid classification model offer better retrieval accuracy, however, we experience a biased classification towards the negative class due to the class imbalance problem when we experience an increase in the number of negative samples due to highly correlated semantic classes. Thus, multiple classifiers based CBIR models become unstable especially in one-against-all classification settings. To address the aforementioned problem, we proposed a CBIR method based on a hybrid features descriptor with the genetic algorithm (GA) and SVM classifier for image retrieval in multi-class scenario. More specifically, we employed the first three color moments, Haar Wavelet, Daubechies Wavelet and Bi-Orthogonal wavelets for features extraction, refine the features using GA and then train the multi-class SVM using one-against-all approach. L₂ Norm is used as a similarity measurement function between the query image and retrieved images against the query image from the image repository. The proposed technique successfully addresses the class imbalance problem in CBIR. Performance of the proposed method is evaluated on four standard datasets i.e. WANG, Oxford Flower, CIFAR-10, and kvasir and compared with 25 different CBIR methods. Experimental results illustrate that our method outperforms the existing state-of-the-art CBIR methods in terms of image retrieval.

近年来，我们见证了网络空间图像生成的巨大增长，这要求开发用于有效内容管理的自动化解决方案。已经提出了基于内容的图像检索（CBIR）系统，以减少对基于文本注释的图像检索系统的依赖性。存在多种基于特征-分类器组合的CBIR方法来分析查询图像的内容以进行相关图像检索。尽管这些方法在单类场景中提供了更好的检索性能，但是，由于不同类图像之间的语义相似性，我们在多类搜索环境中遇到了明显的性能下降。基于混合分类模型的CBIR方法提供了更好的检索精度，但是，当我们由于高度相关的语义类而导致否定样本数量增加时，由于类不平衡问题，我们会遇到对否定类的偏向分类。因此，基于多个分类器的CBIR模型变得不稳定，尤其是在对所有分类设置中。为了解决上述问题，我们提出了一种基于混合特征描述符与遗传算法（GA）和SVM分类器的CBIR方法，用于多类场景中的图像检索。更具体地说，我们使用前三个颜色矩，即Haar小波，Daubechies小波和Bi-Orthogonal小波进行特征提取，使用GA细化特征，然后使用反对所有的方法训练多类SVM。基于多个分类器的CBIR模型变得不稳定，尤其是在对所有分类设置中。为了解决上述问题，我们提出了一种基于混合特征描述符与遗传算法（GA）和SVM分类器的CBIR方法，用于多类场景中的图像检索。更具体地说，我们使用前三个颜色矩，即Haar小波，Daubechies小波和Bi-Orthogonal小波进行特征提取，使用GA细化特征，然后使用反对所有的方法训练多类SVM。基于多个分类器的CBIR模型变得不稳定，尤其是在对所有分类设置中。为了解决上述问题，我们提出了一种基于混合特征描述符与遗传算法（GA）和SVM分类器的CBIR方法，用于多类场景中的图像检索。更具体地说，我们使用前三个颜色矩，即Haar小波，Daubechies小波和Bi-Orthogonal小波进行特征提取，使用GA细化特征，然后使用反对所有的方法训练多类SVM。L ₂范数用作查询图像和针对图像存储库中的查询图像的检索图像之间的相似性度量函数。所提出的技术成功地解决了CBIR中的类不平衡问题。该方法的性能在王，牛津花，CIFAR-10和克瓦西尔这四个标准数据集上进行了评估，并与25种不同的CBIR方法进行了比较。实验结果表明，在图像检索方面，我们的方法优于现有的最新CBIR方法。