Domain Of Content-based Image Retrieval Research Articles

A hybrid CBIR system using novel local tetra angle patterns and color moment features

The exponential growth of communal media platforms including Facebook and Twitter, and the accessibility of low-cost digital capturing devices have generated an enormous number of multimedia content including images. Effective handling of such massive image collection has boosted the development of content-based image retrieval (CBIR) approaches. Researchers have suggested both machine learning and non-learning-based techniques for CBIR. However, machine learning-based methods outperform the non-learning-based methods in the CBIR domain. The CBIR demands the development of reliable descriptors to attain the most appropriate images from the depository and better address the semantic gap problem. To counter these problems, we suggest a novel second-order Local Tetra Angle Patterns (LTAP) to better capture the texture features from the image. LTAPs are computed from adjacent pixels of 0°, 45°, 90°, and 135° using the second-order directional derivatives. Further, we propose a hybrid feature vector by concatenating LTAPs and RGB color features and using the genetic algorithm (GA) to select the finest appropriate features that enhance the image retrieval performance of our system. We employed our hybrid descriptor with the GA to optimize the support vector machine (SVM) for the image classification task and used the Chi-square quadratic distance measure to determine the resemblance between the query image and the images in the repository. Experimental results on three standard datasets including the Corel 1 k, Oxford flower, and CIFAR-10 indicate the effectiveness of the presented system over the contemporary CBIR methods.

Content Based Medical Image Retrieval for Accurate Disease Diagnosis

Introduction: Content Based Image Retrieval (CBIR) system is an innovative technology to retrieve images from various media types. One of the CBIR applications is Content Based Medical Image Retrieval (CBMIR). The image retrieval system retrieves the most similar images from the historical cases, and such systems can only support the physician's decision to diagnose a disease. To extract the useful features from the query image for linking similar types of images is the major challenge in the CBIR domain. The Convolution Neural Network (CNN) can overcome the drawbacks of traditional algorithms, dependent on the low-level feature extraction technique. Objective: The objective of the study is to develop a CNN model with a minimum number of convolution layers and to get the maximum possible accuracy for the CBMIR system. The minimum number of convolution layers reduces the number of mathematical operations and the time for the model's training. It also reduces the number of training parameters, like weights and bias. Thus, it reduces the memory requirement for the model storage. This work mainly focused on developing an optimized CNN model for the CBMIR system. Such systems can only support the physicians' decision to diagnose a disease from the images and retrieve the relevant cases to help the doctor decide the precise treatment. Methods: The deep learning-based model is proposed in this paper. The experiment is done with several convolution layers and various optimizers to get the maximum accuracy with a minimum number of convolution layers. Thus, the ten-layer CNN model is developed from scratch and used to derive the training and testing images' features and classify the test image. Once the image class is identified, the most relevant images are determined based on the Euclidean distance between the query features and database features of the identified class. Based on this distance, the most relevant images are displayed from the respective class of images. The general dataset CIFAR10, which has 60,000 images of 10 different classes, and the medical dataset IRMA, which has 2508 images of 9 various classes, have been used to analyze the proposed method. The proposed model is also applied for the medical x-ray image dataset of chest disease and compared with the other pre-trained models. Results: The accuracy and the average precision rate are the measurement parameters utilized to compare the proposed model with different machine learning techniques. The accuracy of the proposed model for the CIFAR10 dataset is 93.9%, which is better than the state-of-the-art methods. After the success for the general dataset, the model is also tested for the medical dataset. For the x-ray images of the IRMA dataset, it is 86.53%, which is better than the different pre-trained model results. The model is also tested for the other x-ray dataset, which is utilized to identify chest-related disease. The average precision rate for such a dataset is 97.25%. Also, the proposed model fulfills the major challenge of the semantic gap. The semantic gap of the proposed model for the chest disease dataset is 2.75%, and for the IRMA dataset, it is 13.47%. Also, only ten convolution layers are utilized in the proposed model, which is very small in number compared to the other pre-trained models. Conclusion: The proposed technique shows remarkable improvement in performance metrics over CNN-based state-of-the-art methods. It also offers a significant improvement in performance metrics over different pre-trained models for the two different medical x-ray image datasets.

Content Based Medical Image Retrieval for Accurate Disease Diagnosis

Introduction: Content Based Image Retrieval (CBIR) system is an innovative technology to retrieve images from various media types. One of the CBIR applications is Content Based Medical Image Retrieval (CBMIR). The image retrieval system retrieves the most similar images from the historical cases, and such systems can only support the physician's decision to diagnose a disease. To extract the useful features from the query image for linking similar types of images is the major challenge in the CBIR domain. The Convolution Neural Network (CNN) can overcome the drawbacks of traditional algorithms, dependent on the low-level feature extraction technique. Objective: The objective of the study is to develop a CNN model with a minimum number of convolution layers and to get the maximum possible accuracy for the CBMIR system. The minimum number of convolution layers reduces the number of mathematical operations and the time for the model's training. It also reduces the number of training parameters, like weights and bias. Thus, it reduces the memory requirement for the model storage. This work mainly focused on developing an optimized CNN model for the CBMIR system. Such systems can only support the physicians' decision to diagnose a disease from the images and retrieve the relevant cases to help the doctor decide the precise treatment. Methods: The deep learning-based model is proposed in this paper. The experiment is done with several convolution layers and various optimizers to get the maximum accuracy with a minimum number of convolution layers. Thus, the ten-layer CNN model is developed from scratch and used to derive the training and testing images' features and classify the test image. Once the image class is identified, the most relevant images are determined based on the Euclidean distance between the query features and database features of the identified class. Based on this distance, the most relevant images are displayed from the respective class of images. The general dataset CIFAR10, which has 60,000 images of 10 different classes, and the medical dataset IRMA, which has 2508 images of 9 various classes, have been used to analyze the proposed method. The proposed model is also applied for the medical x-ray image dataset of chest disease and compared with the other pre-trained models. Results: The accuracy and the average precision rate are the measurement parameters utilized to compare the proposed model with different machine learning techniques. The accuracy of the proposed model for the CIFAR10 dataset is 93.9%, which is better than the state-of-the-art methods. After the success for the general dataset, the model is also tested for the medical dataset. For the x-ray images of the IRMA dataset, it is 86.53%, which is better than the different pre-trained model results. The model is also tested for the other x-ray dataset, which is utilized to identify chest-related disease. The average precision rate for such a dataset is 97.25%. Also, the proposed model fulfills the major challenge of the semantic gap. The semantic gap of the proposed model for the chest disease dataset is 2.75%, and for the IRMA dataset, it is 13.47%. Also, only ten convolution layers are utilized in the proposed model, which is very small in number compared to the other pre-trained models. Conclusion: The proposed technique shows remarkable improvement in performance metrics over CNN-based state-of-the-art methods. It also offers a significant improvement in performance metrics over different pre-trained models for the two different medical x-ray image datasets.

Content-based image retrieval for fabric images: A survey

In <span>recent years, a great deal of research has been conducted in the area of fabric image retrieval, especially the identification and classification of visual features. One of the challenges associated with the domain of content-based image retrieval (CBIR) is the semantic gap between low-level visual features and high-level human perceptions. Generally, CBIR includes two main components, namely feature extraction and similarity measurement. Therefore, this research aims to determine the content-based image retrieval for fabric using feature extraction techniques grouped into traditional methods and convolutional neural networks (CNN). Traditional descriptors deal with low-level features, while CNN addresses the high-level, called semantic features. Traditional descriptors have the advantage of shorter computation time and reduced system requirements. Meanwhile, CNN descriptors, which handle high-level features tailored to human perceptions, deal with large amounts of data and require a great deal of computation time. In general, the features of a CNN's fully connected layers are used for matching query and database images. In several studies, the extracted features of the CNN's convolutional layer were used for image retrieval. At the end of the CNN layer, hash codes are added to reduce </span>search time.

Enhanced bag of visual words representations for content based image retrieval: a comparative study

The exponential growth of digital image data poses numerous open problems to computer vision researchers. In this regard, designing an efficient and more accurate mechanism that finds and retrieve desired images from large repositories is of greater importance. To this end, various types of content based image retrieval (CBIR) systems have been developed. A typical CBIR system enables the search and retrieval of desired images from large databases that are similar to a given query image by means of automatically extracted visual features from image pixels. In CBIR domain, the bag of visual words (BoVW) model is one of the most widely used feature representation scheme and there exist a number of image retrieval frameworks based on BoVW model. It has been observed that most of them demonstrated promising results for the task of medium and large scale image retrieval. However, image retrieval literature lacks a comparative evaluation of these extended BoVW formulations. To this end, this paper aims to categorize and evaluate the existing BoVW model based formulations for the task of content based image retrieval. The commonly used datasets and the evaluation metrics to assess the retrieval effectiveness of these existing models are discussed. Moreover, quantitative evaluation of state of the art image retrieval systems based on BoVW model is also provided. Finally, certain promising directions for future research are proposed on the basis of the existing models and the demand from real-world.

Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform.

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features. The feature plays a significant role in image processing. The powerful representation of an image is known as feature vector and feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. In this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique will be better. The effectiveness of the CBIR approach is fundamentally based on feature extraction. In image processing errands like object recognition and image retrieval feature descriptor is an immense among the most essential step. The main idea of CBIR is that it can search related images to an image passed as query from a dataset got by using distance metrics. The proposed method is explained for image retrieval constructed on YCbCr color with canny edge histogram and discrete wavelet transform. The combination of edge of histogram and discrete wavelet transform increase the performance of image retrieval framework for content based search. The execution of different wavelets is additionally contrasted with discover the suitability of specific wavelet work for image retrieval. The proposed algorithm is prepared and tried to implement for Wang image database. For Image Retrieval Purpose, Artificial Neural Networks (ANN) is used and applied on standard dataset in CBIR domain. The execution of the recommended descriptors is assessed by computing both Precision and Recall values and compared with different other proposed methods with demonstrate the predominance of our method. The efficiency and effectiveness of the proposed approach outperforms the existing research in term of average precision and recall values.

Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

One of the major requirements of content based image retrieval (CBIR) systems is to ensure meaningful image retrieval against query images. The performance of these systems is severely degraded by the inclusion of image content which does not contain the objects of interest in an image during the image representation phase. Segmentation of the images is considered as a solution but there is no technique that can guarantee the object extraction in a robust way. Another limitation of the segmentation is that most of the image segmentation techniques are slow and their results are not reliable. To overcome these problems, a bandelet transform based image representation technique is presented in this paper, which reliably returns the information about the major objects found in an image. For image retrieval purposes, artificial neural networks (ANN) are applied and the performance of the system and achievement is evaluated on three standard data sets used in the domain of CBIR.

Comparing semantically-blind and semantically-aware landscape similarity measures with application to query-by-content and regionalization

Local landscapes can be assessed for a degree of mutual similarity between their patterns using a methodology pioneered in the domain of Content-Based Image Retrieval (CBIR). They are represented by multivariate histograms of selected pattern features and their similarity is calculated as the similarity between those histograms. Potential similarity functions include semantically-blind measures — designed to compare only the patterns without taking into account intrinsic similarities between the features, and semantically-aware measures — that also take into account intrinsic (semantic) alikeness between landscape features. In this paper we compare the performance of these two types of landscape similarity measures. Two measures are evaluated, the semantically-blind Jensen–Shanon (JS) and the semantically-aware Earth Mover's Distance (EMD). The performance of these two measures is evaluated empirically by running tests pertaining to their potential practical applications: a query-by-content and regionalization into landscape types. Variants of the JS and EMD corresponding to 1D and 2D histograms are applied to the two test sites taken from the NLCD 2006. Each test site is divided into 400 3×3km areal units serving as local landscapes. For each test site extensive query-by-content and regionalization calculations are run using different variants of JS and EMD. The results are compared using statistics and visual assessment. The EMD has been found to perform better than the JS, but the difference in performance is not pronounced, especially if a 1D histogram representation of landscapes is used. For the 2D histogram representation of landscapes the EMD consistently yields better results than the JS but suffers from high computational cost. The EMD also enables the comparison of landscapes having different legends.

Content-based facial image retrieval using constrained independent component analysis

Content-based image retrieval (CBIR) is a method of searching, browsing, and querying images according to their content. In this paper, we focus on a specific domain of CBIR that involves the development of a content-based facial image retrieval system based on the constrained independent component analysis (cICA). Originating from independent component analysis (ICA), cICA is a source separation technique that uses priori constraints to extract desired independent components (ICs) from data. By providing query images as the constraints to the cICA, the ICs that share similar probabilistic features with the queries from the database can be extracted. Then, these extracted ICs are used to evaluate the rank of each image according to the query. In our approach, we demonstrate that, in addition to a single image-based query, a compound query with multiple query images can be used to search for images with compounding feature content. The experimental results of our CBIR system tested with different facial databases show that our system can improve retrieval performance by using a compound query. Furthermore, our system allows for online processing without the need to learn query images.