Image Retrieval Process Research Articles

Montage based 3D Medical Image Retrieval from Traumatic Brain Injury Cohort using Deep Convolutional Neural Network.

Brain imaging analysis on clinically acquired computed tomography (CT) is essential for the diagnosis, risk prediction of progression, and treatment of the structural phenotypes of traumatic brain injury (TBI). However, in real clinical imaging scenarios, entire body CT images (e.g., neck, abdomen, chest, pelvis) are typically captured along with whole brain CT scans. For instance, in a typical sample of clinical TBI imaging cohort, only ~15% of CT scans actually contain whole brain CT images suitable for volumetric brain analyses; the remaining are partial brain or non-brain images. Therefore, a manual image retrieval process is typically required to isolate the whole brain CT scans from the entire cohort. However, the manual image retrieval is time and resource consuming and even more difficult for the larger cohorts. To alleviate the manual efforts, in this paper we propose an automated 3D medical image retrieval pipeline, called deep montage-based image retrieval (dMIR), which performs classification on 2D montage images via a deep convolutional neural network. The novelty of the proposed method for image processing is to characterize the medical image retrieval task based on the montage images. In a cohort of 2000 clinically acquired TBI scans, 794 scans were used as training data, 206 scans were used as validation data, and the remaining 1000 scans were used as testing data. The proposed achieved accuracy=1.0, recall=1.0, precision=1.0, f1=1.0 for validation data, while achieved accuracy=0.988, recall=0.962, precision=0.962, f1=0.962 for testing data. Thus, the proposed dMIR is able to perform accurate CT whole brain image retrieval from large-scale clinical cohorts.

Shape Based Image Retrieval Using Fused Features

For content-based image retrieval, the shape is one of the most important discriminatory elements. The form captures most of the perceptual information of the observed objects on images in many applications, while colour and texture can often be omitted without affecting the performance of the retrieval. Unfortunately, there may be significant changes in shape, such as deformation, scaling, changes in orientation noise, and partial concealment. Accurate shape description remains, therefore, a challenging technical issue. The study performs experimental analysis to identify the problem. The adoption of the MPEG-7 and KIMIA-99 standard has significant importance to simplify the image retrieval process. The Fourier Descriptors, Moment-Based Features, Hierarchical Centroids and Histogram of Oriented Gradients have been applied for extraction of images from datasets. The fusion of features has been done by Discriminant Correlation Analysis and Direct Concatenation of features it has been evident that by fusion of features we obtained approximately 90% accurate and better results.

Medical image retrieval using self-organising map on texture features

The process of capturing, transfer and sharing of information in the form of digital images have become easier due to the use of advanced technologies. Retrieval of desired images from these huge collections of image databases is one of the popular research areas and has its applications in various fields. An image set consists of images containing objects of different colours, shapes, orientations and sizes. The surface texture of the object in an image may also vary from another object in a different image. These factors make the process of image retrieval a difficult one. In this paper, Self-Organising Map is applied on local texture features for organising the brain magnetic resonance images according to their similarity. The correlation among the pixels is considered for the retrieval of most similar images to the input query image. The experimental results obtained prove the effectiveness of the proposed method for medical images.

A hierarchical CBIR framework using adaptive tetrolet transform and novel histograms from color and shape features

Content-Based Image Retrieval (CBIR) systems retrieve the most analogous images from the image database with respect to a given query image based on the texture, shape, and/or color image features. These three image features can be used alone for the image retrieval or also can be used together for the retrieval purpose. In hierarchical CBIR system, three image features are extracted in proper order to discard the irrelevant images in each hierarchy level for reducing the image search space. In this paper, the authors have proposed a three-level hierarchical CBIR system/framework where, each level of the hierarchy uses either texture, shape or color image features to reduce the size of the image database by discarding the irrelevant images and at final level of the hierarchy, it will extract the most analogous images from the reduced image database. We have used adaptive tetrolet transform to extract the texture features from the regions of interest of the images. To extract the shape features of the image, a novel edge joint histogram has been proposed which uses the orientation of the edge pixels and their distance from the origin together to create a novel joint histogram. For color feature extraction, another color channel correlation histogram has been introduced. The order of the three different feature extraction processes on each level of the hierarchy is not rigid because it is difficult to predict the proper order for the highest retrieval. In the experiment, we have considered all possible order of the texture, shape and color features for image retrieval process. The retrieval experiments have been carried out in six different types of standard image databases and results show that the performance of proposed CBIR system has been increased significantly as compared to the other state-of-arts CBIR systems.

The Role and Utilization of CNN in Automatic Logo Based Document Image Retrieval Methods

Automatic logo based document image retrieval process is an essential and mostly used method in the feature extraction applications. In this paper the architecture of Convolutional Neural Network (CNN) was elaborately explained with pictorial representations in order to understand the complex Convolutional Neural Networks process in a simplified way. The main objective of this paper is to effectively utilize the CNN in the process of automatic logo based document image retrieval methods.

MDCBIR-MF: multimedia data for content-based image retrieval by using multiple features

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is an open research problem. In the service of multimedia service, the requirement of Multimedia Indexing Technology is increasing to retrieve and search for interesting data from huge Internet. Since the traditional retrieval method, which is using textual index, has limitation to handle the multimedia data in current Internet, alternatively, the more efficient representation method is needed. 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 visual appearance. The color, shape, and texture are the examples of low-level image features. The feature combination that is also known as feature fusion is applied in CBIR to increase the performance, a single feature is not robust to the transformations that are in the image datasets. This paper represents a new Content-Based Image Retrieval (CBIR) technique to fuse the color and texture features to extract local features as our feature vector. The features are created for each image and stored as a feature vector in the database. The proposed research is divided into three phases that feature extraction, similarities match, and performance evaluation. Color Moments (CM) are used for Color features and extract the Texture features, used Gabor Wavelet and Discrete Wavelet transform. To enhance the power of feature vector representation, Color and Edge Directivity Descriptor (CEDD) is also included in the feature vector. We selected this combination, as these features are reported intuitive, compact and robust for image representation. We evaluated the performance of our proposed research by using the Corel, Corel-1500, and Ground Truth (GT) images dataset. The average precision and recall measures are used to evaluate the performance of the proposed research. The proposed approach is efficient in term of feature extraction and the efficiency and effectiveness of the proposed research outperform the existing research in term of average precision and recall values.

Heterogeneous SoC-based acceleration of MPEG-7 compliance image retrieval process

With the growing amount of multimedial content over the internet and broadcast systems, mechanisms for efficient information organization, manipulation and transmission are becoming indispensable. Optimization of the multimedia search and retrieval processes is nowadays an important area of development due to the difficulty to browse, filter and manage that big amount of data. The adoption of the MPEG-7 standard has a significant importance to simplify the image retrieval process. However, performance issues are still relevant when the retrieval must be accomplished in real time. This work presents an innovative and efficient approach of a Content-Based Retrieval Process using metric spaces implemented in heterogeneous resources according to the demand of computational power. Several implementations were made and comparative results are shown evidencing the benefits of the proposed approach.

MF‐Re‐Rank: A modality feature‐based Re‐Ranking model for medical image retrieval

One of the main challenges in medical image retrieval is the increasing volume of image data, which render it difficult for domain experts to find relevant information from large data sets. Effective and efficient medical image retrieval systems are required to better manage medical image information. Text‐based image retrieval (TBIR) was very successful in retrieving images with textual descriptions. Several TBIR approaches rely on models based on bag‐of‐words approaches, in which the image retrieval problem turns into one of standard text‐based information retrieval; where the meanings and values of specific medical entities in the text and metadata are ignored in the image representation and retrieval process. However, we believe that TBIR should extract specific medical entities and terms and then exploit these elements to achieve better image retrieval results. Therefore, we propose a novel reranking method based on medical‐image‐dependent features. These features are manually selected by a medical expert from imaging modalities and medical terminology. First, we represent queries and images using only medical‐image‐dependent features such as image modality and image scale. Second, we exploit the defined features in a new reranking method for medical image retrieval. Our motivation is the large influence of image modality in medical image retrieval and its impact on image‐relevance scores. To evaluate our approach, we performed a series of experiments on the medical ImageCLEF data sets from 2009 to 2013. The BM25 model, a language model, and an image‐relevance feedback model are used as baselines to evaluate our approach. The experimental results show that compared to the BM25 model, the proposed model significantly enhances image retrieval performance. We also compared our approach with other state‐of‐the‐art approaches and show that our approach performs comparably to those of the top three runs in the official ImageCLEF competition.

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.

Weighted feature voting technique for content-based image retrieval

A content-based image retrieval process is used to retrieve most similar images to a query from a large database of images on the basis of extracted features. Matching measures are used to find similar images by measuring how the query features are close to the features of other images in the database. In this paper, a multi-features system is proposed which incorporates more than one feature in the retrieval process. The weights of these features are calculated based on the precision of each feature to reflect its importance in the retrieval process. These weights are used in a weighted feature voting technique to incorporate the role of each feature in extracting the relevant images. Also, different distance measures are used to get the highest precision of each feature. The results of applying the multi-features and multi-distances measures technique outperform other existing methods with accuracy 86.5% for Wang database, 86.5% for UW database and 85% for Caltech101 database.

Multi resolution feature combined with ODBTC technique for robust CBIR system

Content based image retrieval (CBIR) is a system that retrieves a set of images that most resembles the query image. The technology is used in many applications. Currently used image content retrieval method is ordered-dither block truncation coding (ODBTC). This method is used to produce image content descriptors. In this system, it gives only an average accuracy of 70.5%. Our aim is to create a more robust and accurate system for CBIR. For this purpose in addition to colour cooccurrence feature (CCF) and bit pattern features (BPF), contourlet and wavelet features from the query image is extracted for image retrieval process. In our experiment the system is first tested with ODBTC and wavelet and then ODBTC and contourlet. The results obtained with ODBTC and contourlet is more accurate and produced accuracy 91.5%. The dataset used for our experiment is CorelDB.

Weighted Feature Voting Technique for Content Based Image Retrieval

A content-based image retrieval process is used to retrieve most similar images to a query from a large database of images on the basis of extracted features. Matching measures are used to find similar images by measuring how the query features are close to the features of other images in the database. In this paper, a multi-features system is proposed which incorporates more than one feature in the retrieval process. The weights of these features are calculated based on the precision of each feature to reflect its importance in the retrieval process. These weights are used in a weighted feature voting technique to incorporate the role of each feature in extracting the relevant images. Also, different distance measures are used to get the highest precision of each feature. The results of applying the multi-features and multi-distances measures technique outperform other existing methods with accuracy 86.5% for Wang database, 86.5% for UW database and 85% for Caltech101 database.

A Study of Retrieval Methods of Multi-Dimensional Images in Different Domains

Multiple amount of multi-dimensional images are designed and most of them are available on internet at free of cost. The 3D images include three characteristics namely width, height, and depth. The images which are created as 3D can describe the geometry in terms of 3D co-ordinates. These co-ordinates help to obtain the object from the image much easier and accurate. In this paper, we presented a review about the Multi-dimensional image retrieval. Multi-dimensional image retrieval is a process of extracting the relevant 2D or 3D images from the huge database. To perform image retrieval process on large database, several methods like text based, Content based, Annotation based, semantic based, and sketch based were used. The image retrieval techniques are mostly used in the fields like Digital library, medical, forensic science, and so on. A systematic literature review has been shown for image retrieval methods reported on 2010 to 2017. The aim of this article is to show the various concept and efforts of different authors on image retrieval technique.

Co-occurrence of adjacent sparse local ternary patterns: A feature descriptor for texture and face image retrieval

Searching for similar images in large databases is a time-consuming work and an efficient image retrieval system is valuable in this situation. Texture is a prominent feature of image which can extract contents of image, hence a good texture feature extractor is necessary for content based image retrieval process. In this paper, a new texture descriptor is developed which is a combination of Local Ternary Pattern (LTP) and gray level co-occurrence matrix (GLCM). This feature descriptor which is named as CoALTP, inherits the attributes of both LTP and GLCM. First LTPs of pixels are obtained and then using GLCM in four directions, co-relations between pixel pairs are calculated as features. Two texture databases and a face images dataset are used for evaluation of proposed descriptor and results are compared with other local feature descriptors.

A comparative study of hash based approximate nearest neighbor learning and its application in image retrieval

Plenty of data are available due to the growth of digital technology that creates a high expectation in retrieving the relevant images, accurately and efficiently for a given query image. For searching the relevant images efficiently for the Large Scale dataset, the searching algorithm should have fast access capability. The existing Exact Nearest Neighbor search performs in linear time and so it takes more time as both the dataset and data dimension increases. As a remedy to provide sub-linear/logarithmic time complexity, usage of Approximate Nearest Neighbor (ANN) algorithms is emerging at a rapid rate. This paper discusses about the importance of ANN and their general classification; the different categories involved in Learning to Hash has been analyzed with their pros and cons; different bit assignment types and methods to minimize the Quantization Errors have been reviewed along with its merits and demerits. Therefore, it serves to increase the efficiency of the Image Retrieval process in Large Scale.

Feel the image: The role of emotions in the image-seeking process

Images are characterized by a complex system of attributes. One of the most elusive properties of an image is its emotiveness – the ability to trigger an emotional reaction in the viewer. Prior research has demonstrated that users react to image emotiveness, yet to date there is little theoretical understanding and scarce empirical evidence as to the role played by emotiveness in the image retrieval process. Our study aims to fill this gap and investigates how users perceive the emotional content of the image-seeking task; explicate this emotional content in search keywords; and react to image emotiveness in selecting relevant images. Using a multimethod approach that combines quantitative and qualitative analyses, we performed three experiments where participants searched for images, both within the predefined set of images and in an open Web environment. Our findings suggest that although seekers rarely explicate image emotiveness in search keywords, their decision to select relevant images is largely dependent on their perception of the emotional content of the task and images. We discuss implications for research on emotions in image retrieval, as well as practical implications for designers of search systems.

Fast uniform content-based satellite image registration using the scale-invariant feature transform descriptor

Content-based satellite image registration is a difficult issue in the fields of remote sensing and image processing. The difficulty is more significant in the case of matching multisource remote sensing images which suffer from illumination, rotation, and source differences. The scale-invariant feature transform (SIFT) algorithm has been used successfully in satellite image registration problems. Also, many researchers have applied a local SIFT descriptor to improve the image retrieval process. Despite its robustness, this algorithm has some difficulties with the quality and quantity of the extracted local feature points in multisource remote sensing. Furthermore, high dimensionality of the local features extracted by SIFT results in time-consuming computational processes alongside high storage requirements for saving the relevant information, which are important factors in content-based image retrieval (CBIR) applications. In this paper, a novel method is introduced to transform the local SIFT features to global features for multisource remote sensing. The quality and quantity of SIFT local features have been enhanced by applying contrast equalization on images in a pre-processing stage. Considering the local features of each image in the reference database as a separate class, linear discriminant analysis (LDA) is used to transform the local features to global features while reducing dimensionality of the feature space. This will also significantly reduce the computational time and storage required. Applying the trained kernel on verification data and mapping them showed a successful retrieval rate of 91.67% for test feature points.

Fuzzy generalized median graphs computation: Application to content-based document retrieval

Fuzzy median graph is an important new concept that can represent a set of fuzzy graphs by a representative fuzzy graph prototype. However, the computation of a fuzzy median graph remains a computationally expensive task. In this paper, we propose a new approximate algorithm for the computation of the Fuzzy Generalized Median Graph (FGMG) based on Fuzzy Attributed Relational Graph (FARG) embedding in a suitable vector space in order to capture the maximum information in graphs and to improve the accuracy and speed of document image retrieval processing. In this study, we focus on the application of FGMGs to the Content-based Document Retrieval (CBDR) problem. Experiments on real and synthetic databases containing a large number of FARGs with large sizes show that a CBDR using the FGMG as a dataset representative yields better results than an exhaustive and sequential retrieval in terms of gains in accuracy and time processing.

Semantic image retrieval for complex queries using a knowledge parser

In order to improve the retrieval accuracy of image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to combining image retrieval processing with rich semantics and knowledge-based methods. In this paper, we aim at improving text-based image retrieval for complex natural language queries by using a semantic parser (Knowledge Parser or K-Parser). From text written in natural language, the K-parser extracts a graphical semantic representation of the objects involved, their properties as well as their relations. We analyze both the image textual captions and the natural language queries with the K-parser. As a technical solution, we leverage RDF in two ways: first, we store the parsed image captions as RDF triples; second, we translate image queries into SPARQL queries. When applied to the Flickr8k dataset with a set of 16 custom queries, we notice that the K-parser exhibits some biases that negatively affect the accuracy of the queries. We propose two techniques to address the weaknesses: (1) we introduce a set of rules to transform the output of K-parser and fix some basic, recurrent parsing mistakes that occur on the captions of Flickr8k; (2) we leverage two popular commonsense knowledge databases, ConceptNet and WordNet, to raise the accuracy of queries on broad concepts. Using those two techniques, we can fix most of the initial retrieval errors, and accurately execute our set of 16 queries on the Flickr8k dataset.

A semi-supervised probabilistic model for clustering large databases of complex images

Image content clustering is an effective way to organize large databases thereby making the content based image retrieval process much easier. However, clustering of images with varied background and foreground is quite challenging. In this paper, we propose a novel image content clustering paradigm suitable for clustering large and diverse image databases. In our approach images are represented in a continuous domain based on a probabilistic Gaussian Mixture Model (GMM) with the images modeled as mixture of Gaussian distributions in the selected feature space. The distance metric between the Gaussian distributions is defined in the sense of Kullback–Leibler (KL) divergence. The clustering is done using a semi-supervised learning framework where labeled data in the form of cluster templates is used to classify the unlabelled data. The clusters are formed around initially chosen seeds and are updated in the due course based on user inputs. In our clustering approach the user interaction is done in a structured way as to get maximum inputs from the user in a limited time. We propose two methods to carry out the structured user interaction using which the cluster templates are updated to improve the quality of the clusters formed. The proposed method is experimentally evaluated on benchmark datasets that are specifically chosen to include a wide variation of images around a common theme that is typically encountered in applications like photo-summarization and poses a major semantic gap challenge to conventional clustering approaches. The experimental results presented demonstrate the effectiveness of the proposed approach.