Evaluating Image Retrieval Methods: A Comparative Analysis

There is a growing trend for using content‐based image retrieval (CBIR) systems these days because of the constantly growing interest in digital content. Therefore, the ability of the CBIR to perform the CBIR process will depend on the feature extraction process and its basis, for the retrieval will be done on. Numerous researchers put forward various techniques for feature extraction to enhance the nature of the system. Since features play a very key role in enhancing performance, various features can be used collectively to attain the requisite goal. To retain this in mind, we present in this paper a multifeature fusion system, where three features are integrated and form one feature to improve the situation of retrieval. For this purpose, scale‐invariant feature transform (SIFT), speeded‐up robust features (SURF), and histogram of oriented gradients (HOG) features are adopted. These features are common features that deliver information about the shape of the object and for matching purposes, two techniques of distance matching such as Euclidean and Hausdrauff distance are adopted. To assess the performance of the proposed multifeature‐based CBIR approach, experiments were conducted with the usage of a MATLAB simulator. The Corel‐1000 dataset, consisting of 10,000 images in 100 semantic classes, turned into applied, with each magnificence containing 100 images. A subset of 2500 images across 50 semantic classes was used to train the system. This research aligns with industry, innovation, and infrastructure by contributing to advancements in image processing and retrieval systems. Key characteristic descriptors, along with SIFT, SURF, HOG, texture, and multicharacteristic combinations, were extracted for retrieval functions. The results display that the usage of the Hausdrauff distance as a similarity degree outperforms Euclidean distance, accomplishing retrieval accuracies of 80.02% for HOG, 77.9% for SIFT, 79. 8% for SURF, 77.2% for texture, and 84.2% for multicharacteristic combinations, surpassing Euclidean distance results via 1.7%–3.6% across capabilities. These findings underscore the effectiveness of Hausdrauff distance in enhancing retrieval precision within the CBIR framework.

As the use of digital media increases, effective retrieval and management techniques become more important. In order to extract useful information from this huge amount of data, many content based image retrieval (CBIR) systems have been developed. A typical CBIR system captures image features such as color, texture, or shape of objects in the query image and tries to retrieve images from the database with similar features. The main advantage of CBIR systems with relevance feedback is that these systems take into account the gap between the high-level concepts and low-level features and subjectivity of human perception of visual content. In this paper, a CBIR system based on color histogram has been presented and a new approach for image storage and retrieval called association-based image retrieval (ABIR) has been studied. The results of both systems have been shown.

Multimodal image retrieval, which involves retrieving images using various modalities such as text, audio, or other images, has significant research importance due to its wide-ranging applications and potential to enhance user experiences across multiple domains. Traditional image retrieval systems, content-based image retrieval (CBIR) systems rely solely on visual features, which can be limiting. By integrating multiple modalities, multimodal retrieval systems can offer more robust and accurate results. A research problem is considered multimodal when it integrates information from more than one type of data source. In multimodal image retrieval (MMIR) systems, one form of data is used to search for outcomes in the same or different modalities. In contrast, cross-modal systems strictly retrieve information from a different modality. A significant challenge in these systems is the effective comparison of input-output queries from different data types, due to their basic forms and the subjective nature of content similarity. Researchers have proposed various techniques to address this challenge and to bridge the semantic gap in information retrieval across different modalities. This article presents comprehensive analysis of various research works pertained in this multimodal image retrieval. The results and comparative analysis of current research works on benchmark datasets have also been discussed. At the end of the paper, some of open issues are presented for future research directions.

An image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the large database that matches the user’s requirements in similarity evaluations such as image content similarity, edge, and colour similarity. Retrieving images based on the contents which are colour, texture, and shape is called content-based image retrieval (CBIR). This paper discusses and describes about the colour features technique for image retrieval systems. Several colour features technique and algorithms produced by the previous researcher are used to calculate the similarity between extracted features. This paper also describes about the specific technique about the colour basis features and combined features (hybrid techniques) between colour and shape features.

Content-based object organization for efficient image retrieval in image databases

In this paper, a multi-instance learning based CBIR (content-based image retrieval) approach is presented, and multi-instance learning method is applied in CBIR, in order to deal with the inherent ambiguity of images. First of all the whole image is regards as a multi-instance bag, secondly the image is partitioned into multi-regions by using adaptive k-means image segmentation method, and then query images posed by the user are transformed into corresponding positive and negative bags and a EM-DD(expectation maximization diverse density) algorithm is employed for image retrieval and relevance feedback. Finally, it makes the users get satisfying result.

Medical image indexing and storage is gaining increased importance. Indexing and retrieval of these images efficiently is becomes an essential task. Indexing of medical images using text or numbers is a cumbersome task, difficult to memorize and time consuming. Indexing and Retrieval of images can be done through query by text and query by image which is also known as content based image retrieval (CBIR). Different medical images possess different texture and shape features. This paper presents a novel and hybrid approach of managing a huge medical image database and retrieving a medical image from the database by fusion of shape and texture features. Image is retrieved by comparing the features of query image and images of database. We attach some document information with each medical image such as patient identity, diseases, age, and case history. It can be used for further diagnostic and analysis purpose. Further the proposed algorithm also updates the database automatically if new query is found. MATLAB ® 7.01 and its image processing toolbox have been used to implement the algorithm.

The techniques of clustering and space transformation have been successfully used in the past to solve a number of pattern recognition problems. In this article, the authors propose a new approach to content‐based image retrieval (CBIR) that uses (a) a newly proposed similarity‐preserving space transformation method to transform the original low‐level image space into a high‐level vector space that enables efficient query processing, and (b) a clustering scheme that further improves the efficiency of our retrieval system. This combination is unique and the resulting system provides synergistic advantages of using both clustering and space transformation. The proposed space transformation method is shown to preserve the order of the distances in the transformed feature space. This strategy makes this approach to retrieval generic as it can be applied to object types, other than images, and feature spaces more general than metric spaces. The CBIR approach uses the inexpensive “estimated” distance in the transformed space, as opposed to the computationally inefficient “real” distance in the original space, to retrieve the desired results for a given query image. The authors also provide a theoretical analysis of the complexity of their CBIR approach when used for color‐based retrieval, which shows that it is computationally more efficient than other comparable approaches. An extensive set of experiments to test the efficiency and effectiveness of the proposed approach has been performed. The results show that the approach offers superior response time (improvement of 1–2 orders of magnitude compared to retrieval approaches that either use pruning techniques like indexing, clustering, etc., or space transformation, but not both) with sufficiently high retrieval accuracy.

Introduction: Content-based image retrieval (CBIR) retrieves the images from the vast image repositories. Research in the CBIR domain gets attention due to the massive number of images generated by the mobiles and various image-capturing machines. Objectives: Any image retrieval system’s primary goal is to reduce the semantic gap between low-level features and high-level perception. Methods: The CBIR techniques are classified into multiple categories based on the feature extraction and retrieval mechanism. These categories are feature-based, machine-learning-based, and deep-learning-based methods. The pioneer techniques for each category are explained in detail in this chapter. Results: The comparative analysis has been done to highlight the advantages of techniques over others. Conclusion: The various application area of the CBIR has been described. The most evolving application is the content-based medical image retrieval (CBMIR) system. The applicability of the CBMIR system in different medical science domains is explained in detail.

One of the main challenges in medical image retrieval is the increasing volume of image data, which render it difficult for domain experts to find relevant information from large data sets. Effective and efficient medical image retrieval systems are required to better manage medical image information. Text‐based image retrieval (TBIR) was very successful in retrieving images with textual descriptions. Several TBIR approaches rely on models based on bag‐of‐words approaches, in which the image retrieval problem turns into one of standard text‐based information retrieval; where the meanings and values of specific medical entities in the text and metadata are ignored in the image representation and retrieval process. However, we believe that TBIR should extract specific medical entities and terms and then exploit these elements to achieve better image retrieval results. Therefore, we propose a novel reranking method based on medical‐image‐dependent features. These features are manually selected by a medical expert from imaging modalities and medical terminology. First, we represent queries and images using only medical‐image‐dependent features such as image modality and image scale. Second, we exploit the defined features in a new reranking method for medical image retrieval. Our motivation is the large influence of image modality in medical image retrieval and its impact on image‐relevance scores. To evaluate our approach, we performed a series of experiments on the medical ImageCLEF data sets from 2009 to 2013. The BM25 model, a language model, and an image‐relevance feedback model are used as baselines to evaluate our approach. The experimental results show that compared to the BM25 model, the proposed model significantly enhances image retrieval performance. We also compared our approach with other state‐of‐the‐art approaches and show that our approach performs comparably to those of the top three runs in the official ImageCLEF competition.

Finding an image from a large set of images is an extremely difficult problem. One solution is to label images manually, but this is very expensive, time consuming and infeasible for many applications. Furthermore, the labeling process depends on the semantic accuracy in describing the image. Therefore many Content based Image Retrieval (CBIR) systems are developed to extract low-level features for describing the image content. However, this approach decreases the human interaction with the system due to the semantic gap between low-level features and high-level concepts. In this study we make use of fuzzy logic to improve CBIR by allowing users to express their requirements in words, the natural way of human communication. In our system the image is represented by a Fuzzy Attributed Relational Graph (FARG) that describes each object in the image, its attributes and spatial relation. The texture and color attributes are computed in a way that model the Human Vision System (HSV). We proposed a new approach for graph matching that resemble the human thinking process. The proposed system is evaluated by different users with different perspectives and is found to match users' satisfaction to a high degree.

Image retrieval is a critical task in many fields such as indexing and Content-Based Image Retrieval is a tried and tested method for image retrieval. With advances in graphics and visualization techniques, image retrieval should be easy, quick and flexible. The aim of this work is to develop an efficient image indexing and retrieval system to collate similar images or images based on conditioned queries in the form of an image itself. The indexing method has been performed using wavelet transform and related mechanisms. Its detailing can easily entail description from a broad range to a narrow range. The indexing algorithm applies a Haar Wavelet-Based Perceptual Similarity Index (HaarPSI) for Image Quality Assessment. The HaarPSI utilizes the coefficients obtained from a Haar wavelet decomposition to assess local similarities between two images, as well as the relative importance of image areas. Results show that the algorithm used is able to retrieve and display images with high retrieval accuracy and medium relevance.The proposed methodology has been tested against known image retrieval techniques and was found to be comparable and in some cases better than most prior known techniques. The testing was performed on images of varied types, from multiple datasets and sizes. The accuracy was found to be 99% in most cases. All in all, this study introduces a simple and convenient way of offline image searches on desktop computers and provides a stepping stone to future content-based image retrieval systems built for similar purposes.

The theme of the work presented here is image retrieval using texture patterns generated from Haar transform matrix and image bitmaps. Different texture patterns namely '4-pattern', '16-pattern', '64-pattern', '256-pattern' and '1024-pattern' are generated using Haar transform matrix and then compared with the bitmap of an image to generate the feature vector as the matching number of ones and minus ones per texture pattern. The proposed content based image retrieval (CBIR) techniques are tested on a generic image database having 1000 images spread across 11 categories. For each proposed CBIR technique 55 queries (randomly selected 5 per category) are fired on the image database. To compare the performance of image retrieval techniques, crossover point of average precision and recall values of all the queries are computed per image retrieval technique. Ameliorated performance (higher precision and recall values) has been observed with the proposed methods compared to the colour averaging based image retrieval techniques. Further the performance of proposed image retrieval methods is enhanced using the combination of original image and even image part. In the discussed image retrieval methods, the combination of original and even image part for 256-pattern texture gives the highest crossover point of precision and recall reflecting better performance.

Medical imaging is becoming a vital component of war on cancer. Tremendous amounts of medical image data are captured and recorded in a digital format during cancer care and cancer research. Facing such an unprecedented volume of image data with heterogeneous image modalities, it is necessary to develop effective and efficient content-based medical image retrieval systems for cancer clinical practice and research. While substantial progress has been made in different areas of content-based image retrieval (CBIR) research, direct applications of existing CBIR techniques to the medical images produced unsatisfactory results, because of the unique characteristics of medical images. In this paper, we develop a new multimodal medical image retrieval approach based on the recent advances in the statistical graphic model and deep learning. Specifically, we first investigate a new extended probabilistic Latent Semantic Analysis model to integrate the visual and textual information from medical images to bridge the semantic gap. We then develop a new deep Boltzmann machine-based multimodal learning model to learn the joint density model from multimodal information in order to derive the missing modality. Experimental results with large volume of real-world medical images have shown that our new approach is a promising solution for the next-generation medical imaging indexing and retrieval system.

Several Deep Learning (DL) and medical image Machine Learning (ML) methods have been investigated for efficient data representations of medical images, such as image classification, Content-Based Image Retrieval (CBIR), and image segmentation. CBIR helps medical professionals make decisions by retrieving similar cases and images from electronic medical image databases. CBIR needs expressive data representations for similar image identification and knowledge discovery in massive medical image databases explored by distinct algorithmic methods. In this study, an Improved Whale Optimization Algorithm with Deep Learning-Driven Retinal Fundus Image Grading and Retrieval (IWOADL-RFIGR) approach was developed. The presented IWOADL-RFIGR method mainly focused on retrieving and classifying retinal fundus images. The proposed IWOADL-RFIGR method used the Bilateral Filtering (BF) method to preprocess the retinal images, a lightweight Convolutional Neural Network (CNN) based on scratch learning with Euclidean distance-based similarity measurement for image retrieval, and the Least Square Support Vector Machine (LS-SVM) model for image classification. Finally, the IWOA was used as a hyperparameter optimization technique to improve overall performance. The experimental validation of the IWOADL-RFIGR model on a benchmark dataset exhibited better performance than other models.