# A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion
## 2 Related Work  
CBIR is based mainly on three techniques: semantic, text, and content [4]. With the rapidly increasing requirement of CBIR in digital libraries, military, education, and architectural design, CBIR has become a more active research topic in recent years [5]. In [27], [PERSON] proposed a technique for retrieving images that focuses on bags of visual words with the triangle histogram's weighted average. The proposed method uses bags of word models and spatial image contents. This approach reduces the overfitting problem on a dictionary of larger size. SIFT feature vector was used and then the k-means clustering technique was used so that the nearest features are clustered or Grouped. The authors used the Hellinger kernel function to calculate how similar the query picture and database images are. SVM with the Hellinger kernel was applied due to its simplicity in classification. To evaluate the scaling factor, the SVM Hellinger kernel function has been fixed using 10-fold cross-validation. Although the visual words are depicted in a histogram without considering where they are in the 2D image space, the primary issue is the lack of spatial information in BoVW-based image representation [28]. The authors in [29] proposed a CBIR method that used color, texture, shape, and grey-level co-occurrence matrix (GLCM) featuring contrast, correlation, homogeneity, and energy of images to retrieve similar pictures from the database. The Corel dataset was used to extract similar images and retrieve them using the meta-heuristic algorithm. [PERSON] [30] proposed a technique for retrieving images by combining the features of edge directions, texture features, color histogram, edge histogram, etc., using the deep learning model CNN. In the proposed method, content-based images were extracted from the image database. After a series of operations, such as selection removal edge directions, texture features, color histogram, and edge histogram, features were obtained and saved as small signature files. Signatures were matched to the content-based signature because similar images have similar signatures. Weights were used to standardize the distance coefficients after measuring the distances between the various characteristics. The distance values were used to save and index these normalized coefficients. The proposed model provides only 96% accuracy on the dataset of more than 1000 images. However, this accuracy can be improved by using some image pre-processing techniques. The aim of [31] was to introduce a method for image retrieval constructed on YCbCr color with a canny edge histogram and discrete wavelet transform. This method increases the performance of the content-based image retrieval framework. To identify image edges, [PERSON] presented a CBIR approach based on color characteristics and the YCbCr color space [31]. To create a final RGB image containing edge and histogram information, the edge features were obtained using the Canny-edge feature descriptor and merged with the Y-luminance, Cr, and invariant Cb matrices. The discrete wavelet transforms served as the final feature vector, and the Manhattan distance was used to compare the query image to database images. The proposed system's accuracy was 73%. [PERSON] et al. [32] proposed a system that focuses on the fusion of two feature descriptors: binary robust invariant scalable key points (BRISK) and scale-invariant feature transform (SIFT). Due to the SIFT descriptor's varying illumination, rotation, noise, and invariance to scale, it can only reliably recognize objects 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. In classification-based applications, the BRISK descriptor offers high-quality and flexible performance. When applied to poorly localized key points in an image along an object's edges, the BRISK descriptor outperforms the SIFT descriptor [32]. However, the proposed system achieved only 39.1% Mean Average Precision (MAP). [PERSON] et al.