An Image Retrieval Method Based on Content Self-Organized and Interactive

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.

Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be able to take advantage of the log data of users' feedback to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback schemes to learn effectively the correlation between low-level image features and high-level concepts. Given the error-prone nature of log data, we present a novel learning technique, named soft label support vector machine, to tackle the noisy data problem. Extensive experiments are designed and conducted to evaluate the proposed algorithms based on the COREL image data set. The promising experimental results validate the effectiveness of our log-based relevance feedback scheme empirically.

Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be able to take advantage of the log data of users' feedback to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback schemes to learn effectively the correlation between low-level image features and high-level concepts. Given the error-prone nature of log data, we present a novel learning technique, named soft label support vector machine, to tackle the noisy data problem. Extensive experiments are designed and conducted to evaluate the proposed algorithms based on the COREL image data set. The promising experimental results validate the effectiveness of our log-based relevance feedback scheme empirically.

With the rapid growing volume of images in medical databases, development of efficient image retrieval systems to retrieve relevant or similar images to a query image has become an active research area. Despite many efforts to improve the performance of techniques for accurate image retrieval, its success in biomedicine thus far has been quite limited. This article presents an adaptive content-based image retrieval (CBIR) system for improving the performance of image retrieval in mammographic databases. In this work, the authors propose a new relevance feedback approach based on incremental learning with support vector machine (SVM) regression. Also, the authors present a new local perturbation method to further improve the performance of the proposed relevance feedback system. The approaches enable efficient online learning by adapting the current trained model to changes prompted by the user's relevance feedback, avoiding the burden of retraining the CBIR system. To demonstrate the proposed image retrieval system, the authors used two mammogram data sets: A set of 76 mammograms scored based on geometrical similarity and a larger set of 200 mammograms scored by expert radiologists based on pathological findings. The experimental results show that the proposed relevance feedback strategy improves the retrieval precision for both data sets while achieving high efficiency compared to offline SVM. For the data set of 200 mammograms, the authors obtained an average precision of 0.48 and an area under the precision-recall curve of 0.79. In addition, using the same database, the authors achieved a high pathology matching rate greater than 80% between the query and the top retrieved images after relevance feedback. Using mammographic databases, the results demonstrate that the proposed approach is more accurate than the model without using relevance feedback not only in image retrieval but also in pathology matching while maintaining its effectiveness for online relevance feedback applications.

This paper presents an image representation and matching framework for image categorization in medical image archives. Categorization enables one to determine automatically, based on the image content, the examined body region and imaging modality. It is a basic step in content-based image retrieval (CBIR) systems, the goal of which is to augment text-based search with visual information analysis. CBIR systems are currently being integrated with picture archiving and communication systems for increasing the overall search capabilities and tools available to radiologists. The proposed methodology is comprised of a continuous and probabilistic image representation scheme using Gaussian mixture modeling (GMM) along with information-theoretic image matching via the Kullback-Leibler (KL) measure. The GMM-KL framework is used for matching and categorizing X-ray images by body regions. A multidimensional feature space is used to represent the image input, including intensity, texture, and spatial information. Unsupervised clustering via the GMM is used to extract coherent regions in feature space that are then used in the matching process. A dominant characteristic of the radiological images is their poor contrast and large intensity variations. This presents a challenge to matching among the images, and is handled via an illumination-invariant representation. The GMM-KL framework is evaluated for image categorization and image retrieval on a dataset of 1500 radiological images. A classification rate of 97.5% was achieved. The classification results compare favorably with reported global and local representation schemes. Precision versus recall curves indicate a strong retrieval result as compared with other state-of-the-art retrieval techniques. Finally, category models are learned and results are presented for comparing images to learned category models.

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.

Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database.Image contents are plays significant role for image retrieval.There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design.Each image has three contents such as: color, texture and shape features.Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results.Color histogram and texture features have potential to retrieve similar images on the basis of their properties.As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query.To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solution.

This paper presents a content-based image retrieval (CBIR) system that incorporates real-valued multiple instance learning (MIL) into the user relevance feedback (RF) to learn the user's subjective visual concepts, especially where the user's most interested region and how to map the local feature vector of that region to the high-level concept pattern of the user. RF provides a way to obtain the subjectivity of the user's high-level visual concepts, and MIL enables the automatic learning of the user's high-level concepts. The user interacts with the CBIR system by relevance feedback in a way that the extent to which the image samples retrieved by the system are relevant to the user's intention is labeled. The system in turn applies the MIL method to find user's most interested image region from the feedback. A multilayer neural network that is trained progressively through the feedback and learning procedure is used to map the low-level image features to the high-level concepts.

The use of Computer-Aided Diagnosis in dermatology raises the necessity of integrating Content-Based Image Retrieval (CBIR) technologies. The latter could be helpful to untrained users as a decision support system for skin lesion diagnosis. However, classical CBIR systems perform poorly due to semantic gap. To alleviate this problem, we propose in this paper an intelligent Content-Based Dermoscopic Image Retrieval (CBDIR) system with Relevance Feedback (RF) for melanoma diagnosis that exhibits: efficient and accurate image retrieval as well as visual features extraction that is independent of any specific diagnostic method. After submitting a query image, the proposed system uses linear kernel-based active SVM, combined with histogram intersection-based similarity measure to retrieve the K most similar skin lesion images. The dominant (melanoma, benign) class in this set will be identified as the image query diagnosis. Extensive experiments conducted on our system using a 1097 image database show that the proposed scheme is more effective than CBDIR without the assistance of RF.

Content-based image retrieval (CBIR) has turned into an important and active potential research field with the advance of multimedia and imaging technology. It makes use of image features, such as color, texture and shape, to index images with minimal human intervention. A CBIR system can be used to locate medical images in large databases. In this paper we propose a CBIR system which describes the methodology for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the Adaptive neuro-fuzzy inference system (ANFIS) learning to retrieve similar images from database in two categories: normal and tumoral. A fuzzy classifier has been used, because of the uncertainty in the results of classifier and capacity of learning. ANFIS is a good candidate for our categorization problem. Our proposed CBIR system can locate a query image in the category of normal or tumoral images in the online retrieval part. Finally, using a relevance feedback, we improve the effectiveness of our retrieval system. This research uses the knowledge of the CBIR approach to the application of medical decision support and discrimination between the normal and abnormal medical images based on features. We present and compare the results of the proposed method with the CBIR systems used in recent works. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency compared with the previous works.

Content-based image retrieval (CBIR) has suffered from the lack of linkage between low-level features and high-level semantics. Although relevance feedback (RF) CBIR provides a promising solution involving human interaction, certain query images poorly represented by low-level features still have unsatisfactory retrieval results. An innovative method has been proposed to increase the percentage of relevance of target image database by using graph cuts theory with the maximum-flow/minimum-cut algorithm and relevance feedback. As a result, the database is reformed by keeping relevant images while discarding irrelevant images. The relevance is increased and thus during following RF-CBIR process, previously poorly represented relevant images have higher probability to appear for selection. Better performance and retrieval results can thus be achieved.

To overcome the semantic gap in the retrieval process, the user relevance feedback frame work is added to the current image retrieval system. Relevance feedback method iteratively refines and updates the retrieved result by learning the user-labeled examples to further improves the overall performance. In the proposed scheme feature descriptor derived from the EDBTC compressed data stream. Firstly, a color image is decomposed using EDBTC scheme to produce two new image representations, namely color quantizer and bitmap image by using Improved LBG-VQ (ILBG)algorithm. Two image feature descriptors called Color Histogram Feature (CHF), and Bit Pattern Feature(BPF) can be subsequently generated from the EDBTC color quantizer and its corresponding bitmap image respectively without performing the decoding process. The similarity degree between two images is simply measured with the similarity distance score of their feature descriptor. Second, we presented a relevance feedback (RF) framework for effective image retrieval by using a support vector machine (SVM). Effectiveness of EDBTC feature descriptor is quantitatively examined and compared in the RGB color space as well as in HSI color channel. Extensive experiments shows that there is a significant improvement in retrieval performance with the proposed method using SVMRF compared with the retrieval performance without RF.

Integrated probability function and its application to content-based image retrieval by relevance feedback

Relevance feedback (RF) approaches are use to improve the performance of content-based image retrieval (CBIR) systems. In this paper, a RF approach based on modification of similarity measure using particle swarm optimization (PSO) in a medical X-ray image retrieval system is proposed. In this algorithm, using PSO, the significance of each feature in the similarity measure is modified to image retrieval. This modification causes that good features have major effect in relevant image retrieval. The defined fitness function in PSO uses relevant and irrelevant retrieved images with different strategies, simultaneously. The relevant and irrelevant images are used to exhort and penalize similarity measure, respectively. To evaluate, the proposed RF is integrated to a CBIR system based on semantic classification. In this system, using merging scheme in a hierarchical structure, the overlapped classes are merged together and determined search space for each query image. The proposed RF evaluated on a database consisting of 10000 medical X-ray images of 57 classes. The proposed algorithm provides the improvement, effectiveness more than the literature.

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.