Feature Based Comparison of Text Based Image Retrieval and Context Based Image Retrieval Images

In the context of content-based image retrieval (CBIR) problem, an image is represented by feature vectors called descriptors, whose efficiency is essential to obtain a good performance in the image indexing and retrieval tasks. In this paper, we propose an algorithm to obtain an efficient color-based descriptor, which is a combination of Color Correlogram (CC) and Dominant Color (DC). The color-based descriptors, such as Histogram Intersection (HI) and DC take into account the global distribution of color in an image, while CC takes into account the local color distribution. So the combination of global and local color distribution provides a good image description. By its design, the proposed descriptor is more compact compared with the CC descriptor, which allows reducing computational complexity. Using the Average Retrieval precision (ARP) with different factors the effectiveness of the proposed descriptor is evaluated and compared with the conventional color-based descriptors, such as HI, CC and DC. The image database used in this work contains 500 images with 25 categories randomly selected from the Corel Dataset.

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.

Color histogram is widely used for image indexing in content-based image retrieval (CBIR). A color histogram describes the global color distribution of an image. It is very easy to compute and is insensitive to small changes in viewing positions. However, the histogram is not robust to large appearance changes. Moreover, the histogram might give similar results for different kinds of images if the distributions of colors are same in the images. On the other hand, color correlogram is efficiently used for image indexing in content-based image retrieval. Color correlogram extracts not only the color distribution of pixels in images like color histogram, but also extracts the spatial information of pixels in the images. The characteristic of the color Correlogram to take into account the spatial information as well as the distribution of color pixels greatly attracts the researcher for content based image retrieval. In this paper, we propose the image bin (histogram value divisions) separation technique followed by extracting maxima of frequencies and plotting a correlogram. At first, the histogram is first calculated for an image. After that, it is subdivided into four equal bins. Each bin is subdivided into four more bins and for every such subdivision the maxima of frequencies s calculated. This information is stored in the form of a correlogram. The distance between correlogram of the query image with the corresponding correlogram of database images is calculated. The proposed algorithm is tested on a database comprising a large number of images.

Image retrieval is one of the most interesting and fastest growing research areas in all fields. It is an effective and efficient tool for managing large image databases. In most Content-Based Image Retrieval (CBIR) systems, an image is represented by a set of low-level visual features; hence a direct correlation with high-level semantic information will be absent. Therefore, a gap exists between high-level information and low-level features, which is the main reason that hinders the improvement of the image retrieval accuracy. In this work, main focus is on the semantic based image retrieval system using Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction. Based on the texture features, semantic interpretations are given to the extracted textures. The images are retrieved according to user satisfaction and thereby reduce the semantic gap between low level features and high level features.

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.

Color correlograms are efficiently used for image indexing in content-based image retrieval. Color correlogram extracts not only the color distribution of pixels in images like color histogram, but also extracts the spatial information of pixels in the images. The characteristic of the color correlogram to take into account the spatial information as well as the distribution of color pixels greatly attracts the researcher for content based image retrieval. Even though, a single correlogram is not enough for efficient and robust image retrieval system. In this paper, we propose the use of color correlogram on multiresolution images. The multiresolution color correlogram gives much better retrieval efficiency, but with higher computations. The multiresolution images are generated using the median filters.

Content-based Image retrieval systems extract and retrieve images using their low-level features, such as color, texture, and shape. Nevertheless, these visual contents do not allow a user to formulate semantically meaningful image query. Image annotation systems are a solution to solve the inadequacy of CBIR systems and allow text based image retrieval. There have been several studies on automatic image annotation utilizing machine learning techniques and images' representation with low level features extracted using either global or local methods. However, typically, these approaches suffer from low correlation between the globally assigned annotations and the visual features used to obtain annotations automatically. In this paper, we present an approach to enhance the effectiveness of CBIR using learning based automatic images annotation based on bag of visual word images representation that is created automatically using a set of manually annotated training images. The experimentation is performed with 4,000 annotated images for training, 1000 images for testing from ImageNet. The result has shown 77.5% of performance accuracy. The result of this work is believed to be one step towards enhancing the performance and effectiveness of existing CBIR and minimizing the semantic gap.

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.

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.

Nowadays, the majority of images are in JPEG and MPEG compressed formats, and JPEG2000 is considered to be the next generation of compression standard due to the high- performance of discrete wavelet transform (DWT). It is time- consuming and occupies too much memory in conventional image retrieval ways. In order to solve these problems, we use grading retrieval techniques to implement image retrieval based on discrete cosine transform (DCT) compressed domain and DWT compressed domain. For image retrieval based on DCT domain, we use color features: color moment and color histogram, to describe content of images and propose a new dynamic color space quantization based on color distribution; For image retrieval based on DWT domain, we use texture features as two level feature vectors. The mean and standard deviation of low frequency sub-band coefficients are used as the first level retrieval. The means and standard deviations of selected high frequency sub-band coefficients are used as the second level retrieval. Furthermore, the third level retrieval is achieved by the fast wavelet histogram. Our experiment results clearly show that the two grading image retrieval algorithms work better than other algorithms: store memory is reduced and retrieval accuracy is improved. Index Terms—Content based image retrieval (CBIR), compressed domain, discrete cosine transform (DCT), discrete wavelet transform (DWT), color features, texture features

With the continuous development of science and technology, the popularity and use of projectors, cameras and other electronic products have led to an ever-expanding image source. Due to the enormous workload of text-based image retrieval (TBIR) annotation, content-based image retrieval (CBIR) was proposed to solve this problem. The CBIR has experienced the process featured by an integration of image digital, image semantic, and other multiple image features. CBIR, however, still has shortcomings in embodying the high-level semantics of the image. As the ability of autonomously learning image features, the defects of CBIR in image expression can be effectively solved by deep learning, which thus has become a hot topic in current image expression researches. On the basis of content-based image retrieval technology, some readings and analyses are made in the article for exploring the future development of image retrieval technology in image expression field.

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.

Histogram refinement for texture descriptor based image retrieval

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.

This chapter provides an introduction to information retrieval and image retrieval. Types of image retrieval techniques, i.e., text-based image retrieval and content-based image retrieval techniques are introduced. A brief introduction to visual features like color, texture, and shape is provided. Similarity measures used in content-based image retrieval and performance evaluation of content-based image retrieval techniques are also given. Importance of user interaction in retrieval systems is also discussed.