# A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion
## 5 Comparison with Existing Techniques  
The goal of the [53] study was to suggest an evolutionary method for CBIR that uses k nearest neighbors to choose the best possible solution of the diffusion parameters. To obtain a better set of parameters, genetic algorithms were used for this optimization problem. The proposed system achieved 97.32% precision on the \"oxford5k\", \"Paris 5k\", and \"flicker 1M\" datasets. [PERSON] [54] proposed a method for the CBIR from the \"coil20\" and \"coral1K\" datasets using SIFT feature extractor. SIFT feature extractor results were compared with the other two feature extractors, HOG and SURF. SIFT performs well in retrieving similar images compared to the other two feature descriptors, HOG and SURF. The proposed  
Figure 7: Performance of CBIR on random images.  
model achieved 96% overall precision. The authors of [55] used three datasets, \"Corel 1k\", \"Corel 5K\", and Caltech 256, for the extraction of a similar image in the domain of content-based image retrieval. The proposed methodology provides 99.4% precision on the Corel1K dataset. In this [38] study, the SURF feature descriptor was used for content-based image retrieval. The proposed method provides 86.06% precision on the retrial of the top 10 images from the \"corel1K\" dataset.  
The purpose of the [37] study was to retrieve images from 10 different classes, such as buildings, Africa, elephants, flowers, beaches, horses, buses, dinosaurs, food, and mountains from the \"Corel 1K\" dataset. The entire collection of images was used to extract features, which were then stored as feature vectors. Features were extracted using different feature descriptors, such as Color and Edge Directivity Descriptors (CEDD), HSV histogram, Color moments, and Binarized Statistical Image Features (BSIF). The feature retrieved with the HSV histogram's help involves quantization, color space conversion, and histogram computation. Following the feature extraction, a similarity assessment was performed, comparing the query image to other database images to identify the images that were similar to it. The feature vector was constructed using a variety of distance metrics, including the Mahalanobis, City block, Minkowski distance, Euclidean, Chebyshev, cosine, and L1 and L2 distances, to determine how similar the query image and the other images were. The proposed model has a precision rate of 87.5%. Similar to this [56] study, a novel CNN with a relevant feedback method was proposed for CBIR. The proposed method was tested on three datasets, \"OT\", \"Caltech 101\", and \"Corel 1K\". The proposed CNN performs best on Caltech 101 dataset and provides 98.78% precision.  
The authors of [57] proposed a bi-layer system for CBIR divided into two modules: the first module was designed to extract the features in terms of color, texture, and shape from the images, and the second module has two layers. To extract the form, texture, and indices of the M most similar images to the query image, all images in the \"Corel 1K\" dataset were compared to it. In the second layer, \(M\) images are compared with the query image for shape and color feature space retrieved from the previous layer. Finally, \(F\) images that are more similar to the query image are obtained as an output. The overall average precision of the proposed module was 92.2%. The authors of [58] used deep earning models such as VGG19, Inception v3, dense net, mobile net, and Inception ResNet for the CBIR Image DB2000, Caltech101, and Corel 1K dataset. Inception ResNet provided a higher precision rate of 96.12% among all these models. Figure 8 depicts a comparison between the proposed system and current methods, and Table 5 lists the comparison in terms of precision.  
Figure 8: Comparison with existing techniques.