Visual-Interactive k-NDN Method (VIK): A Novel Approach to Visualize and Interact with Content-Based Image Retrieval Systems Regarding Similarity and Diversity

In the field of computer vision and image processing CBIR (Content based image retrieval) system become an active research area. The CBIR technique involves feature extraction based on image features (like color, shape and texture) and defining rules for comparing image. A classifier is also used by authors for image classification. For this purpose an image database taken from James Z. Wang research group website is used by many authors. Due to enormous increase in the size of database traditional methods of image retrieval by using keywords does not provide accurate result. Thus the concept of CBIR system is emerged. In last couple of years many method of feature extraction is developed but since now researcher are not able to develop a system which has gained high accuracy of human visual perception in distinguishing pictures. Therefore earlier researchers have concentrated to increase the retrieval accuracy of CBIR system by using different combination of features. The objective of this paper is to present review on a CBIR technique which combined four kinds of features set including every kind of feature for developing feature vector. Color based features (histogram), statistical features (mean, median and standard deviation), spatial features (Gabor feature and wavelet features) and texture features (GLCM) is used for forming feature vector. A SVM classifier is used with CBIR system to increase retrieval accuracy. SVM (support vector machine) is a machine learning classifier which provides good classification accuracy. Keywords: CBIR, SVM, NN, GLCM, HSV Color space, Euclidean distance matrix.

In this paper, we present a novel approach for medical content based image retrieval (searching for images which are pathologically similar to a given example) and demonstrate its performance on a dataset containing maxillofacial lesions. Recently, distributed databases at hospitals and CT scanning centers are related through Picture Archiving and Communication Systems (PACS). Hence, a content based image retrieval system is helpful for radiologists in medical diagnosis. In our proposed framework, a feature vector is extracted for each image and SIFT sparse codes are employed in this step. After feature extraction based on sparse coding and maximum pooling, we have utilized different similarity measures such as Euclidean norm, Manhattan distance and SVM classifier to choose most relevant images to the query image. We have evaluated our proposed framework on a dataset containing 2023 images belonging to 5 different categories: cleft, impaction, fracture, maxillary sinus cyst and healthy. Classification rate of 97.3% and precision versus recall curve indicate the effectiveness of sparse coding in content-based medical image retrieval.

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.

Research in content-based image retrieval (CBIR) today is a lively discipline and expanding in breadth. Content-based image retrieval is a computer vision technique to the image retrieval problem of searching for digital images in huge databases. Weather forecasting, data mining, remote sensing, medical imaging, education, crime prevention and management of earth resources are a few domains where content-based image retrieval technique is in huge demand. To improve visual similarity search and image retrieval process in content-based image retrieval many studies have been conducted and methods developed in recent years, but there are a few issues that need to be addressed. This paper explores the current practices in content-based image retrieval and their effectiveness.

Techniques of Content-Based Image Retrieval (CBIR) employ a mathematical representation of an image (also called feature vector), to characterize the image in the retrieval process. The feature vector-based representation of an image in CBIR systems causes the "semantic gap" problem, which is the inconsistency between the low-level image feature representation and the high-level image interpretation. However, the usage of a large number of features to represent an image, which seems to be a solution for the semantic gap, leads to the "dimensionality curse" problem. In this paper, we propose to amend the semantic gap along with the dimensionality curse by a dimensionality reduction method called FTK (Feature Transformation based on K-means). FTK performs feature transformation by clustering the feature vector. It employs the clustering principle of k-means to compact the feature vector space. The results indicate that clustering is an approach well-suited to perform dimensionality reduction in CBIR systems.

In a content-based image retrieval (CBIR) system, indexing feature vectors and the similarity measure between feature vectors are two key factors for retrieval performance. We present a new CBIR system with statistical-model based image feature extraction in the wavelet domain and a Kullback divergence based similarity measure. A two component Gaussian mixture model (GMM) in the wavelet domain is employed and the model parameters are used to form features for image indexing. A new Kullback divergence based similarity measure is then presented for image retrieval. The experimental results demonstrate that the similarity measure based on the Kullback divergence is more effective than conventional similarity measures, such as the city-block distance and the Euclidean distance. It is shown that the new CBIR system, with the combination of the GMM and the new Kullback divergence based similarity measure, outperforms most other methods in retrieval performance for texture images, while keeping a comparable level of computational complexity.

The amount of digital medical image data is increasing rapidly in terms of both quantity and heterogeneity. There exists a great need to format medical image archives so as to facilitate diagnostics and preventive medicine. To achieve this, in the past few decades great efforts have been made to investigate methods of applying content-based image retrieval (CBIR) techniques to retrieve images. However, several critical challenges remain. Recently, CBIR research has become intertwined with the fundamental problem of image understanding and it is recognized that computing solutions that bridge the “semantic gap” must capture higher-level domain knowledge of medical end users. We are investigating the incorporation of state-of-the-art visual categorization techniques into conventional CBIR approaches. Visual attention deployment strategies of medical experts serve as an objective measure to help us understand the perceptual and conceptual processes involved in identifying key visual features and selecting diagnostic regions of the images. Understanding these processes will inform and direct feature selection approaches on medical images, such as the dermatological images used in our study. We also explore systematic and effective information integration methods of image data and semantic descriptions with the long-term goals of building efficient human-centered multi-modal interactive CBIR systems.

Content-Based Image Retrieval (CBIR) systems play a crucial role in managing the exponential growth of digital image collections. Their task is to retrieve relevant images by analysing the content of a query image rather than relying on metadata. The idea here is to extract relevant features from the query image and find a similar set of features in other images. The current generation of CBIR systems faces difficulties in bridging the “semantic gap” between image features at low levels and human perception at high levels which reduces retrieval precision. The research targets CBIR performance enhancement by developing state-of-the-art deep learning techniques for feature extraction. We implemented two approaches which included a custom Convolutional Neural Network (CNN) with six convolutional layers and a finetuned ResNet50 model trained on the CIFAR-10 dataset, and the finetuned model demonstrated better performance than numerous existing CBIR systems. The networks extracted 512-dimensional feature vectors from their penultimate layer for subsequent similarity-matching operations. The fine-tuned ResNet50 model delivered better retrieval results than our custom CNN model since it reached a Mean Average Precision (MAP) score of 98.7%, while the custom CNN achieved 87.3%. We built a web-based interface that allows users to perform real-time image retrieval through multiple evaluation metrics.

Radiologists are now confronted with the difficulty of interpreting cross-sectional studies composed of thousands of images. In hospitals, all acquired imaging data are stored in a picture archiving and communication system (PACS). To take advantage of these masses of previously interpreted images, with the ultimate goal to facilitate the diagnosis of new cases, a promising approach would be to integrate a Content-Based Image Retrieval (CBIR) system into PACS. CBIR system performances are inherently limited by the features considered to represent the images. The current state of the art for the extraction of visual features relies on deep learning which requires a sufficient amount of annotated data to learn a generalizable model, such annotated data being rare and difficult to use in medical imaging. At the same time, PACS contain additional information such as radiological reports which supplement visual information carried by the images. We study here how such semantic information, hidden in these reports, can be used to supervise the learning of neuronal models to build a better visual representation of images. In this context our contribution is threefold. We first adapted a contrastive learning approach, which is usually used to learn representation from pairs of positive images in an unsupervised manner, to deal with in-domain medical data. Second, to train such a model to be robust and generalizable with a sufficient amount of data, we propose to re-employ the "dormant" medical imaging literature. Finally, the visual features and the deep models learned in this way, can be considered in CBIR systems as coarse-grained information which can then be fine-tuned in PACS, with more specific images depending on the applications. The obtained experimental result with state of the art contrastive learning methods highlight the interest of this approach.

Content-Based Image Retrieval (CBIR) Systems allow the search of images by similarity employing a numeric representation automatically or semi-automatically obtained from them to perform the search. Nevertheless, the query result does not always bring what the user expected. In this sense, CBIR systems face the semantic gap problem. One way of overcoming this problem is by the addition of diversity in query execution, so that the user can ask the system to return the most varied images regarding some similarity criteria. However, applying diversity on large datasets has a prohibitive computational cost and, moreover, the result often differs from the expected with a resulting subset that has images with high dissimilarity to the query image. In this paper we propose an approach to reduce the computational cost of Content-Based Image Retrieval systems regarding similarity and diversity criteria. The proposed approach employs dataset fractals analysis to estimate a suitable radius for a database subset to perform a similarity query regarding diversity. It selects closer images to the query center and applies the diversity factor to the subset, providing not only a better comprehension of the impact of the diversity factor to the query result, but also an improvement in execution time.

In the current years, advancements in medical imaging have prompted the rise of some massive databases, including the pictures from a different scope of modalities. Content based image retrieval (CBIR) is a kind of search that selects examples from an image set that have a similar content to an input query image. Each CBIR system consists of two substantial factors, feature extraction and similarity measure. Based on the type of these parts, many CBIR systems are proposed. In this paper we present not only how to design a CBIR system for MRI brain Tumor images, but also a comparison between the different implemented CBIR systems and ours. Furthermore, in this research, using a DML (Distance Metric Learning) instead of the rigid distance metric such as Euclidean distance as the Similarity measure has helped the researchers to obtain a better mAP (mean Average Precision) as the result. The experimental results of our CBIR system show that it has a better performance than other rivals in the field. The obtained mean average precision of our CBIR system is 92.41 that is significant in MRI brain tumor.

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.

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.

Image annotation and retrieval has been a popular research topic for decades. Based on published journals from 2012 until 2015, a lot of research and studies has been focused on Content Based Image Retrieval (CBIR). In most cases, CBIR systems that use an image as the input query always face a problem called semantic gap due to the use of low-level features for similarity matching. The semantic gap will consequently reduce the performance of the CBIR systems. To overcome this problem, a new class of CBIR known as Annotation Based Image Retrieval (ABIR) has been developed. The ABIR systems, based on the annotation process of the images, employ bag of words for query. However it is found that employing pure bag of word only is not adequate to solve semantic problem. In this study, an attempt to use a Gaussian Mixture Model (GMM) based approach and spatial related information of the annotated objects has been performed in order to improve the performance of the ABIR systems. From the retrieval experiments, it is found that ABIR could achieve average performance up to 88% which is two times better than the CBIR systems in reducing the semantic problem.

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.