# A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion
###### Abstract  
The multimedia content generated by devices and image processing techniques requires high computation costs to retrieve images similar to the user's query from the database. An annotation-based traditional system of image retrieval is not coherent because pixel-wise matching of images brings significant variations in terms of pattern, storage, and angle. The Content-Based Image Retrieval (CBIR) method is more commonly used in these cases. CBIR efficiently quantifies the likeness between the database images and the query image. CBIR collects images identical to the query image from a huge database and extracts more useful features from the image provided as a query image. Then, it relates and matches these features with the database images' features and retakes them with similar features. In this study, we introduce a novel hybrid deep learning and machine learning-based CBIR system that uses a transfer learning technique and is implemented using two pre-trained deep learning models, ResNet50 and VGG16, and one machine learning model, KNN. We use the transfer learning technique to obtain the features from the images by using these two deep learning (DL) models. The image similarity is calculated using the machine learning (ML) model KNN and Euclidean distance. We build a web interface to show the result of similar images, and the Precision is used as the performance measure of the model that achieved 100%. Our proposed system outperforms other CBIR systems and can be used in many applications that need CBIR, such as digital libraries, historical research, fingerprint identification, and crime prevention. Keywords:Content-Based Image Retrieval (CBIR); pre-trained model; deep learning; machine learning; Euclidean distance +
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Metadata for retrieving images, such as descriptions, keywords, and captions [9]. Textual annotation of images relies on manual labor to associate the keywords with images, which cannot describe the ambiguity of image content and its diversity [10]. CBIR (Content-Based Image Retrieval) systems aim to improve the accuracy of image retrieval by focusing on prominent features of the images. By analyzing the features of an image, CBIR systems can provide more accurate and relevant results for a given search query. However, most of the methods of CBIR used only one to two feature descriptors that provide results with low accuracies, such as the scale-invariant feature transform (SIFT) descriptor, which 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 this paper, pre-trained models of deep learning are used to extract the most prominent features. CBIR is the search that focuses on the image content instead of the Metadata, such as the description associated with the image, keyword, and tags [11]. CBIR was introduced in the 1990s to minimize the problem associated with the laborious text-based image retrieval system [12]. CBIR helps to extract or retake similar image content from a huge database and multimedia applications. It considerably reduces image retrieval time compared to unstructured and tedious browsing [13]. The visual content of the images is retrieved from the database and then stored as feature vectors [14]. The CBIR method calculates the feature vectors of the database and characterizes the visual properties against them. These image signatures or feature vectors act as feature databases and are used for similarity measures between the images of the repository and the query image [15]. CBIR shows the potential usefulness in the past twenty years of development in different real-world applications [16]. Image contents include image structure, shape, contrast, and color. The massive growth of visual information requires a capable system that retrieves images and video libraries from huge databases [7]. Many image retrieval methods exist, such as in [17]; authors used deep auto encoder for CBIR.