# A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion
## 2 Related Work
[33] proposed a method for CBIR from the MRI images by using features of images such as texture, shape, and color. The proposed method retrieves and represents the brain MRI images from a vast database that is closely similar to that query image. The proposed methodology was used for selecting distinct planes (sagittal, transverse, and coronal) in brain MRI images from a dataset of normal and demented people who were assessed. The features were determined using Haralik'sfeatures based on Grey level co-occurrence and histogram-based cumulative distribution function (CDF). The K-Nearest Neighbor algorithm was used for the image retrieval technique to determine the minimum distance or similarity between the query image and the database images. The performance Matrices that were calculated for the model were Precision and Recall. The proposed model obtained 95.5% average accuracy. However, it can be increased by using the DL models for the feature extraction process. In [34], the authors introduced a method that supports cloud server-based local feature extraction such as SIFT, image similarity scoring, and Index building. They proposed a multi-index for SIFT descriptors, the encrypted image was loaded, and the model returned similar images from the database in response to a query image. Exact Euclidean LSH was applied on SIFT descriptors, and secure block identifier computational protocol was used as the building block of image retrieval and multi-index construction. Due to the SIFT descriptor's varying illumination, rotation, noise, and invariance to scale, it can only recognize objects reliably when there is clutter. However, low illumination and poorly located key points inside an image caused SIFT descriptor to fail and lower the performance of CBIR. The purpose of the [35] study was to propose an approach containing the complete structural information about an image extracted from the image by descriptors such as local binary patterns and GLCM.GLCM descriptor, which was used to extract the dominant local binary pattern (LBP) and the statistical traits for classifying textures in images. To reduce computing load during the classification process, the proposed method employs particle swarm optimization (PSO) to reduce the number of features. Support vector machine, decision tree, and K-nearest neighbor, the three classifiers, were trained, and Euclidean distance was employed as a similarity metric. SVM performs the best among these models and provides 85% precision; however, it can be increased. [PERSON] et al., in their [36] study, proposed a new framework for the efficient retrieval of lung disease for a patient with the combination of different descriptors, such as local phase quantization (LPQ), local ternary pattern (LTP), and discrete wavelet transform. Joint mutual information (JMI)-based feature was also used to minimize duplication and select an ideal feature set for retrieving CISs. The similarity was measured to create a balanced graph by combining semantic and visual information in an equal ratio. The final degree of similarity between the images was calculated using the shortest path. The computations were performed with distance metrics such as cosine, city block, correlation, Spearman, and Euclidean. However, the proposed approach is inefficient in terms of computational cost because manual feature descriptors were used, such as LPQ, LTQ, and DWT, to extract features from images that take more time and resources. Authors in [37] used color and edge descriptors for the extraction of features and Euclidean Distance, L1, L2, and cosine to check the similarity. Similarly, in [38], only the SURF feature descriptor was used to extract the visual features. A comparison of related work is listed in Table 1.