Effective Content-based Image Retrieval Research Articles

An Overview of Content-Based Image Retrieval Methods and Techniques

With the development of Internet technology and the popularity of digital devices, Content-Based Image Retrieval (CBIR) has been quickly developed and applied in various fields related to computer vision and artificial intelligence. Currently, it is possible to retrieve related images effectively and efficiently from a large-scale database with an input image. In the past ten years, great efforts have been made for new theories and models of CBIR, and many effective CBIR algorithms have been established. Content-based image retrieval helps to discover identical images in a big dataset that match a query image. The query image's representative feature similarities to the dataset images typically assist in ranking the images for retrieval. There are various past studies on different handicraft feature descriptors according to the visual features that describe the images: color, texture, and shape. However, deep learning has been the dominant alternative to manually planned feature engineering; it automatically takes the features from the data. The current work reviews recent advancements in content-based image retrieval. For a deeper understanding of the advancement, the explanation of current state-of-the-art approaches from various vantage points is also conducted. This review employs a taxonomy encompassing various retrieval networks, classification types, and descriptors and this study will help researchers make more progress in image retrieval

ENHANCING IMAGE RETRIEVAL EFFICIENCY WITH SPATIAL DEPENDENCE MATRIX AND TRANSLATION INVARIANT DISCRETE WAVELET TRANSFORM

The present study describes the implementation of a highly effective content-based image retrieval (CBIR) system. This system utilizes an integrated approach for feature extraction, incorporating the use of a spatial dependence matrix (SDM) to extract texture features from the provided images. Additionally, a translation invariant discrete wavelet transform (TIDWT) is employed for low-level feature extraction. Moreover, the effectiveness of the proposed hybrid Content-Based Image Retrieval (CBIR) system was evaluated using the Tanimoto distance. The results of a comprehensive experimental investigation reveal that the suggested hybrid Content-Based Image Retrieval (CBIR) system exhibits a significant enhancement in efficiency when compared to standard CBIR systems.

Recent developments of content-based image retrieval (CBIR)

With the development of Internet technology and the popularity of digital devices, Content-Based Image Retrieval (CBIR) has been quickly developed and applied in various fields related to computer vision and artificial intelligence. Currently, it is possible to retrieve related images effectively and efficiently from a large scale database with an input image. In the past ten years, great efforts have been made for new theories and models of CBIR and many effective CBIR algorithms have been established. In this paper, we present a survey on the fast developments and applications of CBIR theories and algorithms during the period from 2009 to 2019. We mainly review the technological developments from the viewpoint of image representation and database search. We further summarize the practical applications of CBIR in the fields of fashion image retrieval, person re-identification, e-commerce product retrieval, remote sensing image retrieval and trademark image retrieval. Finally, we discuss the future research directions of CBIR with the challenge of big data and the utilization of deep learning techniques.

Deep learning for fast bronze inscription image retrieval

Collections of bronze inscription images are increasing rapidly. To use these images efficiently, we proposed an effective content-based image retrieval framework using deep learning. Specifically, we extract discriminative local features for image retrieval using the activations of the convolutional neural network and binarize the extracted features for improving the efficiency of image retrieval, firstly. Then, we use the cosine metric and Euclidean metric to calculate the similarity between the query image and dataset images. The result shows that the proposed framework has an impressive accuracy.

Convolution Index based Unsupervised Label Procedure for Efficient Medical Image Exploration

Medical imaging is a forceful idea of various medicinal ideas i.e. malignant growth and other related infections, present days; various kinds of therapeutic pictures are caught and saved in computerized position in medicinal research focuses. Confronting this kind of huge volume of picture information with various sorts of picture modalities, it is critical to execute effective content based image retrieval (CBIR) for restorative research focuses. Picture mark ordering is another actualized strategy for medicinal picture recovery. Traditionally various kinds of CBIR methodologies are proposed to give unsatisfied therapeutic picture recovery results. So that in this paper, propose a Convolution Index based Unsupervised Label (CIUL) way to deal with recover marks of pictures utilizing AI wording. We characterize AI as matrix convex optimization with cluster-based matrix representation which can be utilized to improve the productivity in picture recovery framework.

Content-based image retrieval based on supervised learning and statistical-based moments

Content-based image retrieval (CBIR) system generally retrieves images based on the matching of the query image from all the images of the database. This exhaustive matching and searching slow down the image retrieval process. In this paper, a fast and effective CBIR system is proposed which uses supervised learning-based image management and retrieval techniques. It utilizes machine learning approaches as a prior step for speeding up image retrieval in the large database. For the implementation of this, first, we extract statistical moments and the orthogonal-combination of local binary patterns (OC-LBP)-based computationally light weighted color and texture features. Further, using some ground truth annotation of images, we have trained the multi-class support vector machine (SVM) classifier. This classifier works as a manager and categorizes the remaining images into different libraries. However, at the query time, the same features are extracted and fed to the SVM classifier. SVM detects the class of query and searching is narrowed down to the corresponding library. This supervised model with weighted Euclidean Distance (ED) filters out maximum irrelevant images and speeds up the searching time. This work is evaluated and compared with the conventional model of the CBIR system on two benchmark databases, and it is found that the proposed work is significantly encouraging in terms of retrieval accuracy and response time for the same set of used features.

Scene analysis and search using local features and support vector machine for effective content-based image retrieval

Despite broad investigation in content-based image retrieval (CBIR), issue to lessen the semantic gap between high-level semantics and local attributes of the image is still an important issue. The local attributes of an image such as shape, color, and texture are not sufficient for effective CBIR. Visual similarity is a principal step in CBIR and in the baseline approach. In this article, we introduce a novel approach, which relies on the fusion of visual words of scale-invariant feature transform (SIFT) and binary robust invariant scalable keypoints (BRISK) descriptors based on the visual-bag-of-words approach. The two local feature descriptors are chosen as their fusion adds complementary improvement to CBIR. The SIFT descriptor is capable of detecting objects robustly under cluttering due to its invariance to scale, rotation, noise, and illumination variance. However, SIFT descriptor does not perform well at low illumination or poorly localized keypoints within an image. Due to this reason, the discriminative power of the SIFT descriptor is lost during the quantization process, which also reduces the performance of CBIR. However, the BRISK descriptor provides scale and rotation-invariant scale-space, high quality and adaptive performance in classification based applications. It also performs better at poorly localized keypoints along the edges of an object within an image as compared to the SIFT descriptor. The suggested approach based on the fusion of visual words achieves effective results on the Corel-1K, Corel-1.5K, Corel-5K, and Caltech-256 image repositories as equated to the feature fusion of both descriptors and latest CBIR approaches with the surplus assistances of scalability and fast indexing.

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model.

For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques.

Automated and effective content-based image retrieval for digital mammography.

Nowadays, huge number of mammograms has been generated in hospitals for the diagnosis of breast cancer. Content-based image retrieval (CBIR) can contribute more reliable diagnosis by classifying the query mammograms and retrieving similar mammograms already annotated by diagnostic descriptions and treatment results. Since labels, artifacts, and pectoral muscles present in mammograms can bias the retrieval procedures, automated detection and exclusion of these image noise patterns and/or non-breast regions is an essential pre-processing step. In this study, an efficient and automated CBIR system of mammograms was developed and tested. First, the pre-processing steps including automatic labelling-artifact suppression, automatic pectoral muscle removal, and image enhancement using the adaptive median filter were applied. Next, pre-processed images were segmented using the co-occurrence thresholds based seeded region growing algorithm. Furthermore, a set of image features including shape, histogram based statistical, Gabor, wavelet, and Gray Level Co-occurrence Matrix (GLCM) features, was computed from the segmented region. In order to select the optimal features, a minimum redundancy maximum relevance (mRMR) feature selection method was then applied. Finally, similar images were retrieved using Euclidean distance similarity measure. The comparative experiments conducted with reference to benchmark mammographic images analysis society (MIAS) database confirmed the effectiveness of the proposed work concerning average precision of 72% and 61.30% for normal & abnormal classes of mammograms, respectively.

Comparative Analysis of Distance Metrics for Designing an Effective Content-based Image Retrieval System Using Colour and Texture Features

Comparative Analysis of Distance Metrics for Designing an Effective Content-based Image Retrieval System Using Colour and Texture Features

Hybridized Feature Descriptor (HFD) for Content Based Image Retrieval on Large Image Database

Objectives: The hybridized feature descriptor is constructed to describe the relevance between the query and database images in the database. The input query is in the form of text keywords, specifications, image properties, color/ texture patterns or image itself. Method: The layered approach has been used to design experimental model, which helps to match the image features one after one to prepare the final image ranking. The similarity factor is calculated by SVM based learning model. It can achieve very good results under the limited feature matching paradigm. Findings: The existing model is defeated by this model because of its hybrid feature descriptor solution, proposed by the amalgamation of the color and low ranked features computed with feature fitness validation. The performance has been evaluated by using various performance parameters like accuracy, ranking/index building time, library lookup time, precision, recall etc. Improvement: The proposed model has resulted in effective content based image retrieval system. The experimental result shows the efficiency and robustness of this model.

Line Segment-Based Stitched Multipanel Figure Separation for Effective Biomedical CBIR

We present a novel technique to separate panels from stitched multipanel figures appearing in biomedical research articles. Since such figures may comprise images from different imaging modalities, separating them is a crucial first step for effective biomedical content-based image retrieval (CBIR): multimodal biomedical document classification and/or retrieval, for instance. The method applies local line segment detection based on the gray-level pixel changes. It then applies a line vectorization process that connects prominent broken lines along the panel boundaries while eliminating insignificant line segments within the panels. We validated our fully automatic technique on a set of stitched multipanel biomedical figures extracted from articles within the Open Access subset of PubMed Central® repository, and achieved precision and recall of 87.16% and 83.51%, respectively, in less than 0.461[Formula: see text]s per image, on average. We also reported the recent ImageCLEF 2015 competition results that highlight the usefulness of the proposed work.

An enhanced texture-based image retrieval approach with features selected from integration of feature extraction techniques

Texture is vital in characterising images for effective content-based image retrieval. Integrating features from various feature extraction techniques improves the performance of decision system in comparison to individual techniques as it provides complimentary information as a whole. However, this integration creates a large feature vector which may contain irrelevant and redundant features and hence degrade the performance. Therefore, we propose a three-phase texture-based image retrieval approach for enhanced performance. In the first phase, pool of texture features from seven feature extraction techniques is created. In the second phase, some popular feature selection techniques are applied to this pool to obtain a reduced set of relevant and non-redundant features. In the third phase, three well-known distance measures are utilised to retrieve images based on the reduced features set. The performance of the proposed approach is evaluated on Brodatz dataset. The proposed approach outperforms individual feature extraction techniques.

Retrieval of Remote Sensing Images with Pattern Spectra Descriptors

The rapidly increasing volume of visual Earth Observation data calls for effective content based image retrieval solutions, specifically tailored for their high spatial resolution and heterogeneous content. In this paper, we address this issue with a novel local implementation of the well-known morphological descriptors called pattern spectra. They are computationally efficient histogram-like structures describing the global distribution of arbitrarily defined attributes of connected image components. Besides employing pattern spectra for the first time in this context, our main contribution lies in their dense calculation, at a local scale, thus enabling their combination with sophisticated visual vocabulary strategies. The Merced Landuse/Landcover dataset has been used for comparing the proposed strategy against alternative global and local content description methods, where the introduced approach is shown to yield promising performances.

Hierarchical Visual Perception and Two-Dimensional Compressive Sensing for Effective Content-Based Color Image Retrieval

Although content-based image retrieval (CBIR) has been an active research theme in the computer vision community for over two decades, there are still challenging problems in properly understanding the process in feature extraction and image matching. Consequently, significant research is still required to develop solutions for practical applications, especially in exploring and making the best using of the cognitive aspects of the human vision system. Motivated by three cognitive properties of human vision, namely hierarchical structuring, color perception and embedded compressed sensing, we proposed a novel framework for CBIR. First, we use a hierarchical approach to perform discrete cubic partitioning of the image in the HSV space. Then, we propose a new hierarchical mapping of the image data through the use of hierarchical operators: SGLCM. These features are then integrated in a 2D CS model, which extracts refined features and suppresses noise. Finally, the resultant features are used for similarity-based ranking to perform CBIR. Experiments were performed using two Corel image datasets, i.e., the Corel-1000 dataset which contains 1000 images in 10 image categories and the Corel-10000 dataset which contains 10000 images in 100 image categories where each category contains 100 images. In comparison with three other state-of-the-art approaches, the proposed method has demonstrated much improved retrieval accuracy, especially for images with rich color contents and detail, yet the computational complexity has been significantly reduced to meet the needs for real-time online applications. The implication of the study is that the exploitation of cognitive properties of our human vision systems in effective CBIR. Future research work can be further explored to address some limitations for optimized parameter setting, adaptive feature fusion and improved machine learning.

Hybrid Swarm Intelligence Method for Post Clustering Content Based Image Retrieval

Content Based Image Retrieval is one of the most promising method for image retrieval where searching and retrieving images from large scale image database is a critical task. In Content Based Image Retrieval many visual feature like color, shape, and texture are extracted in order to match query image with stored database images. Matching the query image with each image of large scale database results in large number of disc scans which in turns slows down the systems performance.The proposed work suggested an approach for post clustering Content Based Image Retrieval, in which the database images are clustered into optimized clusters for further retrieval process. Various clustering algorithms are implemented and results are compared. Among all, it is found that hybrid ACPSO algorithm performs better over basic algorithms like k-means, ACO, PSO etc. Hybrid ACPSO has the capability to produce good cluster initialization and form global clustering.This paper discusses work-in-progress where we have implemented till clustering module and intermediate results are produced. These resulted clusters will further be used for effective Content Based Image Retrieval.

Accurate and efficient shape matching approach using vocabularies of multi-feature space representations

Selection of compressed, robust and accurate features is the fundamental ingredient of effective content-based image recognition and retrieval using shape information of objects in the image. In this paper, we present a four-stage system for real-time object recognition and retrieval that employs multiple feature space representation using contour information. In the first stage, we pre-process the shapes to cater for the presence of distortions such as cracks that can significantly distort the contour information of the shape. We then generate multiple feature space representations of shapes to be used later in proposed combination for efficient and accurate retrieval of shapes using a hierarchical indexing structure. To enable real-time image-based shape analysis by enhancing the efficiency and reducing the storage requirement of proposed shape descriptors, we present a quantization approach to generate vocabulary of feature space representation of shapes. These features are then combined in an ensemble for accurate and efficient shape retrieval and recognition in the presence of large shape datasets. The proposed system is evaluated using publicly available shape datasets such as MPEG 7, Swedish leaf and KIMIA 99 datasets. Our approach achieves higher accuracies which are better than state-of-the-art approaches reported in literature whilst looking at a small subset of shapes in dataset.

Development of a Framework for Stereo Image Retrieval With Both Height and Planar Features

The wide availability and increasing number of applications for high-resolution optical satellite stereo images (HrosSIs) have created a surging demand for the development of effective content-based image retrieval methods. However, this is a challenge for existing stereo image retrieval methods since they were designed for stereo images collected from close-range imaging sensors. Thus, successful retrieval of images is not assured given the mismatch between existing methods and the characteristics of HrosSIs. Moreover, none of the existing remote sensing image retrieval methods takes account of the specific characteristics of HrosSIs such as the viewing number and multiview angles. This paper proposes a generic framework to exploit the unique characteristics of HrosSIs data so as to allow efficient and accurate content-based HrosSI retrieval. HrosSIs retrieval is executed by similarity matching between the features obtained from digital surface models (DSMs) and orthoimages, both extracted from the HrosSIs. In addition, the significance of height information for HrosSI retrieval was investigated. A prototype system was designed and implemented for method validation using the ISPRS stereo benchmark test dataset. Experimental results show that the proposed techniques are efficient for HrosSI retrieval. The proposed framework is efficient and suitable for spaceborne stereo images but might also be suitable for airborne stereo images as well. Experimental results also show that height information alone is inefficient and unstable for HrosSI retrieval; however, a combination of height information and planar information is efficient and stable.

Disparity based stereo image retrieval through univariate and bivariate models

The widespread use of stereovision in various application fields has led to the constitution of very huge stereo image databases. Therefore, the design of effective content based image retrieval system devoted to stereo pairs becomes an issue of importance. To this end, we propose in this paper two retrieval methods which combine the visual contents of the stereo images with their corresponding disparity information. After modeling the distribution of their associated wavelet coefficients by the generalized Gaussian statistical model, the resulting distribution parameters are selected as salient features. While the two views are processed separately through a univariate modeling in the first method, the second one exploits the correlation between the views by resorting to a bivariate modeling. Experimental results show the benefits which can be drawn from the proposed retrieval approaches.

WAVELET BASED CONTENT BASED IMAGE RETRIEVAL USING COLOR AND TEXTURE FEATURE EXTRACTION BY GRAY LEVEL COOCURENCE MATRIX AND COLOR COOCURENCE MATRIX

In this study we proposes an effective content based image retrieval by color and texture based on wavelet coefficient method to achieve good retrieval in efficiency. Color feature extraction is done by color Histogram. The texture feature extraction is acquired by Gray Level Coocurence Matrix (GLCM) or Color Coocurence Matrix (CCM). This study provides better result for image retrieval by integrated features. Feature extraction by color Histogram, texture by GLCM, texture by CCM are compared in terms of precision performance measure.