Subspace indexing for extremely high-dimensional CBIR

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 retrieval is an important problem in multimedia systems. It is determined as the process of searching and fetching images from a dataset. Content-based Image Retrieval (CBIR) is a significant and challenging field of research in digital image processing. The CBIR system's essential requirement is to retrieve the relevant information following a query image with higher system output from a large image database. Unfortunately, not all the methods are suitable to be used to get high accuracy of retrieval. Therefore, this research aims to classify the data of query image with the data of image database to get a similar image retrieval using Support Vector Machine (SVM) and validate its accuracy based on the classification for the performance evaluation using the precision-recall measure. The critical point of SVM is to get an optimal hyperplane that separates the data points into two classes. This method was applied to different image databases because the classified-based proposed scheme proved better performance than various existing methods. This project assessed experimental results toward 500 images of the Caltech-256 image dataset to demonstrate the proposed method. For retrieving a similar image following the query image, 20 images from five classes were successfully retrieved by using this method. It will show that the SVM method's average accuracy from all image classes is 94.79%.

In the query by image content (QBIC) project we are studying methods to query large on-line image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, and shape of image objects and regions. Potential applications include medical (`Give me other images that contain a tumor with a texture like this one'), photo-journalism (`Give me images that have blue at the top and red at the bottom'), and many others in art, fashion, cataloging, retailing, and industry. Key issues include derivation and computation of attributes of images and objects that provide useful query functionality, retrieval methods based on similarity as opposed to exact match, query by image example or user drawn image, the user interfaces, query refinement and navigation, high dimensional database indexing, and automatic and semi-automatic database population. We currently have a prototype system written in X/Motif and C running on an RS/6000 that allows a variety of queries, and a test database of over 1000 images and 1000 objects populated from commercially available photo clip art images. In this paper we present the main algorithms for color texture, shape and sketch query that we use, show example query results, and discuss future directions.

With the rapid advancement of digital imaging technologies, and the use of large volume image databases in various applications, it becomes imperative to build an automatic and an efficient image retrieval system. Content Based Image Retrieval (CBIR) is most emerging and vivid research area in computer vision, in which unknown query image assigns to the closest possible similar images available in the database. Current systems mainly use colour, texture, and shape information for image retrieval using similarity measures between query and database images features. Here this work, proposed a classification system that allows recognizing and recovering the class of a query image based on its visual content. This successful categorization of images greatly enhances the performance of retrieval by filtering out irrelevant classes. In this way we have done the comparative analysis of various features as an individual or in combinations, with direct similarity measure and proposed framework. Experimentson benchmark Wang database show that the proposed classification & retrieval framework performs significantly better than the common framework of distances.

Integrating wavelets with clustering and indexing for effective content-based image retrieval

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 image data has grown fast in fields like social media, healthcare, and e-commerce and this has enhanced the demand of quick and precise image retrieval systems. Conventional methods of retrieval that have relied on handcrafted methods of feature extraction and pixel-wise comparison are costly in computer time and cannot withstand changes in image quality, lighting, and view point. To resolve these drawbacks, this project adopts an effective content-based image retrieval framework with a Vision Transformer (ViT) to extract features and FAISS (Facebook AI Similarity Search) to conducted fast similarity search. The system gets compact numerical representations of features of images as a result of a deep learning model and constructs a scale index to search similarity. The FAISS IndexIVFFlat architecture allows to search the nearest-neighbor approximationally with poor scale and low query time by grouping image features into clusters and lessening the search space at query time. Metadata of the indexed images are stored in the system and the top-k most similar images are retrieved according to the distance scores. This method is accurate and fast thus appropriate when dealing with large scale image databases. The system relies on the IndexIVFFlat structure of FAISS to cluster image features into regions which is a significant reduction of the search space that can support meaningful approximate nearest-neighbor queries. Results indicate that the system can be effective in separating useful and useless images that have high similarity (up to 0.819) and low distances (0.220) that ensure the accuracy and scalability of the system to image databases of large scales.

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.

Content-based image retrieval (CBIR) is a hard task which consists in retrieving similar content from a large multimedia database. In the literature, in order to extract descriptors from the images the CBIR techniques use several low-level features such as color, texture, shape, contours, among others. In the recent years, several local descriptors based on the detection of feature points have been used to retrieve the most similar images with different geometric and photometric characteristics. In this paper we propose a CBIR technique that involves the combination of a local descriptor obtained from the Speeded Up Robust Feature (SURF) algorithm together with an effective and fast object matching operation in order to improve the search speed and the retrieval accuracy related to the Mexican archaeological imaging. In order to reduce the computational complexity of the proposed method, Quarter Common Intermediate Format (QCIF) is used previous of computing the SURF descriptor. To measure the performance of the proposed technique the precision and recall metrics are used. The experimental results show the accuracy of the proposed CBIR technique applied to a data base of Mexican culture images that are captured by several environmental conditions and different acquisition equipment.

The increasing number of mobile phones equipped with powerful cameras leads to huge collections of user-generated images. To utilize the information of the images on site, image retrieval systems are becoming more and more popular to search for similar objects in an own image database. As the computational performance and the memory capacity of mobile devices are constantly increasing, this search can often be performed on the device itself. This is feasible, for example, if the images are represented with global image features or if the search is done using EXIF or textual metadata. However, for larger image databases, if multiple users are meant to contribute to a growing image database or if powerful content-based image retrieval methods with local features are required, a server-based image retrieval backend is needed. In this work, we present a content-based image retrieval system with a client server architecture working with local features. On the server side, the scalability to large image databases is addressed with the popular bag-of-word model with state-of-the-art extensions. The client end of the system focuses on a lightweight user interface presenting the most similar images of the database highlighting the visual information which is common with the query image. Additionally, new images can be added to the database making it a powerful and interactive tool for mobile contentbased image retrieval.

The paper addresses image feature characterization and the structuring of large and heterogeneous image databases through the stochasticity or randomness appearance. Measuring stochasticity involves finding suitable representations that can significantly reduce statistical dependencies of any order. Wavelet packet representations provide such a framework for a large class of stochastic processes through an appropriate dictionary of parametric models. From this dictionary and the Kolmogorov stochasticity index, the paper proposes semantic stochasticity templates upon wavelet packet sub-bands in order to provide high level classification and content-based image retrieval. The approach is shown to be relevant for texture images.

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.

We present a content-based image retrieval system, INDI (techniques for Intelligent Navigation in Digital Image databases), that combines the use of low-level pattern recognition techniques, machine learning and an intuitive human-computer interface in order to support intelligent and user-friendly semantic navigation in large image databases. To keep independence from specific image domains and to encompass different search tasks, the system is highly modular and contains a hierarchical mechanism for the adaptive reweighting of similarity measures implemented by dynamically reloadable modules at different semantic levels.

Digital image databases have seen an enormous growth over the last few years. However, since many image collections are poorly indexed or annotated, there is a great need for developing automated, content-based methods that would help users to retrieve images from these databases. In recent times, a lot of attention has been paid to the management of an overwhelming accumulation of rich digital images to support various search strategies. In order to improve the traditional text-based or SQL (Structured Query Language)-based database searches, research has been focused on efficient access to large image databases by the contents of images, such as color, shape, and texture. Content-based image retrieval (CBIR) has become an important research topic that covers a large number of domains like image processing, computer vision, very large databases, and human computer interaction (Smeulders, Worring, Santini, Gupta & Jain, 2000). Several content-based image retrieval systems and methods have recently been developed.

Content based image retrieval is a challenging issue in management of existing large digital image libraries and databases. The accuracy of image retrieval methods is subject to effective extraction of image features such as color, texture, and shape. In this paper we propose a new image retrieval method using contourlet transform coefficients. We use the properties of contourlet coefficients to assign the normal distribution function to the distribution of coefficients in each sub-band. The assigned normal distribution functions are used to extract the texture feature vector at the next stage. Simulation results indicate that the proposed method outperforms other conventional texture image retrieval methods such as, Gabor filter and wavelet transform. Moreover, this method shows a noticeable higher performance compared to another contourlet based method.