Feature selection for content-based image retrieval

Content-based image retrieval (CBIR) techniques will be part of the next generation of search engines. However, even with a suitable retrieval method, real-time image retrieval from a large image database still requires extremely high computational power. In this paper, a Web-based image database indexing and retrieval system is described. It uses fast CBIR with Beowulf-Class parallel computing engine (Super Abacus) to efficiently and effectively shorten the retrieval time. Content-based image retrieval with several parallel algorithms for images which query different color spaces and the compressed-domain of the images are developed to achieve higher efficiency. Text-based query is also supported for images using a conventional annotation approach.

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.

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.

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.

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 image retrieval (CBIR) is an application of computer vision techniques to the image retrieval problem. That is, the problem of searching for digital images in large database. In this paper, we apply an image segmentation technique to an image retrieval system which is designed for the use on mobile devices. Given an image captured by the mobile devices, edge detection and region merging mechanisms are used in this segmentation technique to extract the ROI from a complex background scene. The proposed method automatically merges the regions that are initially segmented by mean shift segmentation, and then effectively extracts the object contour by the labeled regions as either background or foreground. With no users interaction, the experimental results show the method is more effective than other automatic segmentation methods.

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.

Indexing chromatic and achromatic patterns for content-based colour image retrieval

In recent years, with the development of digital image techniques and digital albums in the Internet, the use of digital image retrieval process has increased dramatically. An image retrieval system is a computer system for browsing, searching and retrieving images from large databases of digital images. In order to increase the accuracy of image retrieval, a content-based image retrieval system(CBIR) based on interactive genetic algorithm (IGA) is proposed. Color, texture and edge have been the primitive low level image descriptors in content based image retrieval systems. In this paper we proposed a system that splits the retrieval process into two stages. In the query stage, the feature descriptors of a query image were extracted and then used to evaluate the similarity between the query image and those images in the database. In the evolution stage, the most relevant images were retrieved by using the IGA. IGA is employed to help the users identify the images that are most satisfied to the users' need. The experimental evaluation of the system is based on a 10000 WANG color image database. Experimental results demonstrate the feasibility of the proposed approach.

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.

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.

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.

Region-based image retrieval and relevance feedback are two important methods to bridge the gap between the low-level visual features and the high-level semantic concepts in content-based image retrieval. In this paper, we address the issue of introducing the relevance feedback mechanism into the region-based image retrieval with online feature selection during each feedback round. Our contribution is two-fold. (1) A novel region-based image representation is proposed. Based on a generative model, a fuzzy codebook is extracted from the original region-based features, which represents the images in a uniform real-value feature space. (2) A feature selection criterion is developed and an effective relevance feedback algorithm is implemented in a boosting manner to simultaneously select the optimal features from the fuzzy codebook, and generate a strong ensemble classifier over the selected features. Experimental results show that the proposed scheme can substantially improve the retrieval performance of the region-based image retrieval system.

Semantic gap optimization is the major objective of Content Based Image Retrieval(CBIR) system. It is challenging to derive possible query objectives in case of an image having multiple objects or complex background. The query requirements are defined as the Bag of Words(BoW) and used for retrieval of the similar images. The saliency based segmentation is popularly used to eliminate the background objects that works well when the image is focused on the foreground objects. In case saliency-map fail to extract the foreground object, the feature vectors are equally biased by the background scene and that imposter the actual query objectiveThis paper proposes a two phase framework CBIR system. In the first phase we compute the Local Binary Pattern and the RGB Colour information(LBPC) features of all the images in the large image database. Then the pertinent images are clustered and the image on the cluster centre is assumed as ideal indexed image for the same class. In the second phase, each input image is divided into nine blocks having common central sub-block. The LBPC feature is extracted for all the blocks and only the feature of the block that best matches with the indexed image is stored in the database. The selection of the best block image feature minimizes the effect of the background scene. This approach helps select the potential saliency block of the image with respect to the indexed image. The result section shows the performance of the proposed framework while BoW is defined by the cluster indexed image.

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.