Efficient Content-based Image Retrieval Research Articles

Secure and efficient content-based image retrieval using dominant local patterns and watermark encryption in cloud computing

Secure and efficient content-based image retrieval using dominant local patterns and watermark encryption in cloud computing

A Novel Semantic Content-Based Retrieval System for Hyperspectral Remote Sensing Imagery

With the growing use of hyperspectral remote sensing payloads, there has been a significant increase in the number of hyperspectral remote sensing image archives, leading to a massive amount of collected data. This highlights the need for an efficient content-based hyperspectral image retrieval (CBHIR) system to manage and enable better use of hyperspectral remote-sensing image archives. Conventional CBHIR systems characterize each image by a set of endmembers and then perform image retrieval based on pairwise distance measures. Such an approach significantly increases the computational complexity of the retrieval, mainly when the diversity of materials is high. Those systems also have difficulties in retrieving images containing particular materials with extremely low abundance compared to other materials, which leads to describing image content with inappropriate and/or insufficient spectral features. In this article, a novel CBHIR system to define global hyperspectral image representations based on a semantic approach to differentiate foreground and background image content for different retrieval scenarios is introduced to address these issues. The experiments conducted on a new benchmark archive of multi-label hyperspectral images, which is first introduced in this study, validate the retrieval accuracy and effectiveness of the proposed system. Comparative performance analysis with the state-of-the-art CBHIR systems demonstrates that modeling hyperspectral image content with foreground and background vocabularies has a positive effect on retrieval performance.

Efficient content-based image retrieval using integrated dual deep convolutional neural network

<p>Content-based image retrieval (CBIR) uses the content features for retrieving and searching the images in a given large database. Earlier, different hand feature descriptor designs are researched based on cues that are visual such as shape, colour, and texture used to represent these images. Although, deep learning technologies have widely been applied as an alternative to designing engineering that is dominant for over a decade. The features are automatically learnt through the data. This research work proposes integrated dual deep convolutional neural network (IDD-CNN), IDD-CNN comprises two distinctive CNN, first CNN exploits the features and further custom CNN is designed for exploiting the custom features. Moreover, a novel directed graph is designed that comprises the two blocks i.e. learning block and memory block which helps in finding the similarity among images; since this research considers the large dataset, an optimal strategy is introduced for compact features. Moreover, IDD-CNN is evaluated considering the two distinctive benchmark datasets the oxford dataset considering mean average precision (mAP) metrics and comparative analysis shows IDD-CNN outperforms the other existing model.</p>

An Improved and Efficient Content-Based Medical Image Retrieval Technique vs State-of-Art Texture Feature Extraction Techniques

Areas in the health sector such as X-rays, Dermatology, High-resolution Computed Tomography – HRCT, Endoscopy, Radiology, Cardiology and Magnetic Resonance Imaging – MRI heavily depend on medical images for their activities hence it has become complex for managing and accessing these images from their repositories. As a matter of concern content-based medical image retrieval-CBMIR has been the system proposed by many researchers for handling access to similar medical image(s) as the input image, yet there is a caveat that has to be addressed with respect to which technique best suits CBMIR system with respect to a given performance metric. Medical images are mostly of grayscale and therefore color feature extraction techniques may not work effectively on them. Since there is no clear indication of which of the various texture feature extraction techniques is best suited for a given performance metric, this work seeks to comparatively evaluate the performance of the following state-of-the-art texture feature extraction techniques; Local Binary Pattern (LBP), Gabor Filter, Gray-Level Co-occurrence Matrix (GLCM), Haralick Descriptor, Features from Accelerated Segment Test (FAST) and a Proposed Technique using the metrics; Precision, Recall, F1-score, Mean Squared Error (MSE), Accuracy and Time. The results showed that the proposed technique is best suited for systems focusing on precision with an average precision score of 100% over 10.5k of raw medical images (dataset) using an appreciable minimum time with time complexity of 0(n).

EDBC Algorithm used for Content-Based Image Retrieval

The tremendous increase and om- nipresent accessibility of graphic documents on the network led to the high interest in research on content-based image retrieval (CBIR). This has ce- mented the approach for a massive sum of innovative procedures and schemes, and growing curiosity in allied fields to upkeep such projects. Existing associ- ated theories include efficient Content-based Image Retrieval (CBIR) frame by enacting the content- based image, K-means and hybrid clustering is func- tional over combined lineament vector of information images, texture features. In similar cases it is tight in expressing the user’s semantic Intention knowledge to permit information distribution and reuse, models ought to be managed within repositories, where they might be retrieved upon users’ queries. There is still a lack of adequate tools for incisive/handling visual content. In this paper, a novel algorithm Efficient Density-based Clustering Algorithm (EDBC) is sug- gested for content-based image retrieval technique that will enhance scalability and lower maintenance costs significantly, enhance the efficacy of software development.

Building an efficient content based image retrieval system by changing the database structure

The <span>amount of interest in digital images is growing because of the advent of the World Wide Web. Many different industries are making use of digital images today, including medicine, design, journalism, education, and more. Images can be stored and retrieved using a variety of techniques and methods. Nevertheless, the majority of those engines are dependent on Meta data (key-words, descriptions, tags). Although efficient, costly, and able to target keywords and tags with each image, and these engines have their shortcomings. Though it may be possible to use a filtering system to give more accurate results, there are additional issues.</span>

Exploiting deep and hand-crafted features for texture image retrieval using class membership

• Proposed a novel scheme for texture image retrieval. • Proposed scheme exploits the strength of both hand-crafted and deep features. • Modified distance function is used for image retrieval in wavelet feature space. • Distance function is weighted using output class memberships of the query image. • Efficiency of the proposed method found to be promising. In the modern digital era, with the availability of low-cost hardware like sensors and cameras, a huge amount of image databases are being created for diverse applications. These databases give rise to the need of developing efficient content-based image retrieval (CBIR) systems. Major efforts have been put over the past two decades to develop different global and low-level texture features to build efficient CBIR systems. However, designing texture features that are suitable for distance-based retrieval is always a challenging task. Recently, Convolution Neural Networks have shown promising results for object detection and classification . CNNs are also applied to build classifier-based retrieval systems. However, the classifier-based retrieval methods can retrieve images only from the predicted class. Therefore, the performance of such system greatly depends on classification performance of the classifier . This paper proposes a method that exploits the strength of the Convolutional Neural Networks for predicting the class membership of the query image for all output classes and retrieve images using a modified distance function in the wavelet feature space. The performance of the proposed method is evaluated using three popular texture datasets of varying complexity and found to be superior to all competing methods considered.

An efficient content-based medical image retrieval based on a new Canny steerable texture filter and Brownian motion weighted deep learning neural network

An efficient content-based medical image retrieval based on a new Canny steerable texture filter and Brownian motion weighted deep learning neural network

Efficient content-based image retrieval using deep search and rescue algorithm

Efficient content-based image retrieval using deep search and rescue algorithm

A Study on Design and Development of Framework for Content-based Image Retrieval

CBIR is one of the most widely used approaches for detecting images from an extensive image database. The advanced integration and deployment of computer networking technologies enabled a sudden explosion in the number of ever increasing different types of internet based contents eg: digital images, audio and video content etc. Therefore it leads to a situation where retrieval of those complex data in a short period of time become more challenging. As a primary consequence there is an immense need to develop a novel technique well capable of retrieving such complex information based on their respective content or features. Howere, taking above consideration into account. In this paper, we discuss the reviews on the proposed study formulates an efficient Content Based Image Retrieval (CBIR) framework. The framework also implements a conceptual modelling based on biomedical image retrieval and classification. The study outcomes, found to exhibit better accuracy in retrieving similar images with very less processing time.

An efficient content-based satellite image retrieval system for big data utilizing threshold based checking method

An efficient content-based satellite image retrieval system for big data utilizing threshold based checking method

Integration of Discrete Wavelet Transform, DBSCAN, and Classifiers for Efficient Content Based Image Retrieval

In the domain of computer vision, the efficient representation of an image feature vector for the retrieval of images remains a significant problem. Extensive research has been undertaken on Content-Based Image Retrieval (CBIR) using various descriptors, and machine learning algorithms with certain descriptors have significantly improved the performance of these systems. In this proposed research, a new scheme for CBIR was implemented to address the semantic gap issue and to form an efficient feature vector. This technique was based on the histogram formation of query and dataset images. The auto-correlogram of the images was computed w.r.t RGB format, followed by a moment’s extraction. To form efficient feature vectors, Discrete Wavelet Transform (DWT) in a multi-resolution framework was applied. A codebook was formed using a density-based clustering approach known as Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The similarity index was computed using the Euclidean distance between the feature vector of the query image and the dataset images. Different classifiers, like Support Vector (SVM), K-Nearest Neighbor (KNN), and Decision Tree, were used for the classification of images. The set experiment was performed on three publicly available datasets, and the performance of the proposed framework was compared with another state of the proposed frameworks which have had a positive performance in terms of accuracy.

A scalable sub-graph regularization for efficient content based image retrieval with long-term relevance feedback enhancement

The goal of content-based image retrieval (CBIR) is to search relevant images through the analysis of image content. Manifold Ranking (MR) and Efficient Manifold Ranking (EMR) method has been successfully applied to content-based image retrieval due to its ability to discover underlying geometrical structure of dataset given the query data. But given the image database is scalable, the graph in MR and EMR cannot be extended or updated as their graph size is fixed. In this paper, to solve the above problem, we consider to formulate a sub-graph based on fixed anchors, instead of constructing the graph based on the while dataset, where the anchors are selected by utilizing conventional k-means method and the sub-graph weight matrix is defined by the similarity between any pair-wise anchors. Since the number of anchors is much smaller than the original dataset, updating the sub-graph is much easier than the original graph of whole dataset. Motivated by such sub-graph construction, we then develop an efficient graph regularization framework to predict the ranking scores for the whole data along the sub-graph, where the ranking score is first propagated from query image to the partial anchors, then from partial anchors to all anchors via the sub-graph and finally to the whole dataset. It can also utilize user relevance feedbacks to update the sub-graph so that the discriminative information can be involved to enhance the retrieval performance in a long term. Extensive simulations verify the effectiveness of the proposed method.

Query-by-visual-search: multimodal framework for content-based image retrieval

Content-based image retrieval (CBIR) states the procedure of recovering images having similar visual content against a query image from image datasets. In CBIR, the selection of redundant and irrelevant features from images results in the semantic gap issue, which occurs during feature representation and machine learning process. The robust image representation for effective and efficient image retrieval mainly depends upon robust feature selection and classification, which also reduces the semantic gap problem of CBIR. This paper proposed an innovative method for effective and efficient CBIR. The method uses sparse complementary features for vigorous image representation, optimal feature selection based on locality-preserving projection, fuzzy c-means clustering, and soft label support vector machine for robust image classification. In CBIR, smaller and larger sizes of codebook improve the recall and precision (accuracy) of the system, respectively. Due to this reason, the proposed method introduces complementary features based on a larger size codebook, which is assembled using two small sizes of codebooks to increase CBIR performance. The three well-known image datasets (i.e. Corel-1000, Corel-1500, and Holidays) are used to assess the performance of the proposed method. The experimental evaluation highlights promising results as compared to recent methods of CBIR.

A hybrid late fusion-genetic algorithm approach for enhancing CBIR performance

Accurate discrimination of images features is a main success factor towards efficient content-based image retrieval systems. These features can be extracted using local and/or global descriptors. Researchers efforts showed that, hybrid descriptors reported superior results compared to methods that use single descriptor, where hybridization certainly complements benefits from different perspectives. Genetic Algorithm (GA) is a heuristic computational intelligence approach that can be used to achieve the optimal satisfactory user image retrieval requests. In this paper, a new hybrid efficient and effective evolutionary retrieval approach (CBIR-GAF) based on late fusion of four global descriptors is proposed. Each descriptor produces a list of retrieved similar images to user query image and if these lists are merged correctly by late fusion, the results are improved. Thus, GA occurs to assign different weights to each retrieved image while merging, and then it optimizes these weights with a suitable fitness function to select optimum heterogeneous retrieved images. The proposed approach is evaluated on two benchmark datasets (Inria Holidays and Oxford5k), and reported a promising results where it enhanced the average accuracy in comparison of literature techniques.

Textural and color descriptor fusion for efficient content-based image retrieval algorithm

In communication, medium images are used for various applications such as social websites, education, bio-medical field and industrial applications. Indexing and retrieval of such large image database pose a big problem. The content-based image retrieval (CBIR) approaches are used to select information from the input images using feature descriptors. In this article, CBIR algorithm is proposed with fusion of color and texture descriptors such as binary Gabor pattern (BGP), segmentation based fractal texture analysis (SFTA), edge histogram descriptor (EHD), Gabor wavelet texture features and fuzzy histogram-based descriptors. Laplacian score has employed to reduce feature vector dimensions. Retrieval rate is computed using different distance metrics. Experimentation was performed with ten different classes of 1000 images using Wang database. The simulation results indicate improvement in terms of precision compared with the existing techniques.

SECOND ORDER DERIVATIVE CROSS DIAGONAL MATRIX APPROACH FOR CBIR

In this paper, we propose two descriptors based on second-order derivative tetra patterns for efficient content based image retrieval (CBIR). The proposed descriptors initially encode the first-order derivative along 00 and 900 over the center pixel and also on each of the sampling pixel or referred pixel of a 3x3 window. These derivatives derive ternary directions. This paper computes the relationship between the first-order ternary derivative of center pixel and each referred pixel. This process derives the second-order derivative tetra pattern ( ) for each referred pixel. This paper then computes unit for each 3x3window of tetra patterns. This paper initially computes the histograms of individual color planes of HSV model. The proposed first descriptor integrates individual histograms of HSV plane with , and this derives color based ( ). To reduce the bin size of , this paper divides the 3x3 into the cross and diagonal units and derives the second-order derivative cross and diagonal matrix ( ). The GLCM features are computed on . The proposed descriptors and are tested with popular databases and also compared with the recent methods of CBIR.

Correction to: An efficient content based image retrieval using enhanced multi-trend structure descriptor

Figure 1 in the original article was incorrect. The correct Fig. 1 is shown below with the corrected part in red.

Image retrieval based on gradient-structures histogram

Color perception and orientation selection are very important mechanisms of the human brain that have close relationships with feature extraction and representation. However, extracting low-level features by mimicking these mechanisms remains challenging. To address this problem, we present the gradient-structures histogram as a novel method of content-based image retrieval (CBIR). Its main highlights are: (1) a novel and easy-to-calculate local structure detector, the gradient-structures, which simulates the orientation selection mechanism based on the opponent-color space and connects it with low-level features, (2) a novel discriminative representation method that describes color, intensity and orientation features. It is convenient, as it does not require weight coefficients for color, intensity and orientation. (3) The proposed representation method has the advantages of being histogram-based and having the power to discriminate spatial layout, color and edge cues. The proposed method provides efficient CBIR performance, as demonstrated by comparative experiments in which it significantly outperformed some state-of-the-art methods, including the Bow method, local binary pattern histogram, perceptual uniform descriptor, color volume histogram, color difference histogram, some improved LBP methods and the Tree2Vector method in terms of precision/recall and AUC metrics.

An efficient content based image retrieval using enhanced multi-trend structure descriptor

This paper contributes a new variant of multi-trend structure descriptor (MTSD) for efficient content based image retrieval. The proposed variant of MTSD encodes color/edge orientation/texture quantized values versus orientation of equal, small and large trends instead of color/edge orientation/texture quantized values versus equal, small and large trends. In addition, it also encodes color/edge orientation/texture quantized values versus average location of distribution of pixel values for equal, small and large trends at each orientation. To reduce the time cost of the proposed variant of MTSD with the preservation of its accuracy, the image is decomposed into fine level using discrete Haar wavelet transform and the fine level for the decomposition of an image is determined empirically. Comprehensive experiments are conducted using the benchmark Corel-1k, Corel-5k, Corel-10k, Caltech-101, LIDC-IDRI-CT, VIA/I-ELCAP-CT and OASIS-MRI image datasets and the results evident that the proposed variant of MTSD achieves the state-of-the-art performance for natural, textural and biomedical image retrieval. Precision and recall are the measures used to measure the accuracy. Euclidean similarity measure is used to calculate the similarity information between query and target images.