Content-based Image Retrieval Scheme Research Articles

CBIF-M: A content-based image filtering and retrieval scheme using multi-classifier filtering framework

Recently, the image retrieval process appears to be a challenging task to filter out a huge volume of objectionable images from retrieval. It is very easy for people of all ages to obtain such obscene images with just a few clicks on the internet, which could harm the adolescent. To prevent such serious social problems, this paper introduces a content-based image filtering (CBIF) framework in an image retrieval system. The proposed CBIF approach is split into two components. In the first component, the skin region of exposed human body parts is extracted based on a color segmentation method, and an efficient feature vector generation process is introduced based on frequent skin color pixels and scale-invariant structural features. A novel texture feature representation is presented in the retrieval and filtering process obtained by block-paired local binary pattern (BP-LBP). The final component introduces a novel image filtering framework for the retrieved images found from the first component based on a majority of voting method on three different machine learning classifiers backed by SVM, MLP, and CNN. The retrieval efficiency is analyzed with standard performance indicators such as precision, recall, and F-score on two large datasets: NPDI and NSFW.

A DCT probability histogram-based ROI features for content-based natural and medical image retrieval applications

The effectiveness of a Content-based Image Retrieval (CBIR) system depends on selecting representative and salient visual features. Capturing the salient image features for forming a feature vector is an essential part of the CBIR process, as it can significantly affect retrieval efficiency. Most images contain a visually prominent region that perceives maximum image semantic information, known as Regions-of-Interest (ROIs). This paper suggests a new CBIR scheme that employs a Graph-based Visual Saliency (GBVS) map to extract the ROI of the image. Further, block-level Discrete Cosine Transforms (DCT) and subsequent histograms are computed for the DC and significant AC coefficients of the extracted ROI image. In addition, a low-dimensional feature vector is formed from the modified histograms. Subsequently, six statistical features from the background scene have also been computed for the final feature vector construction. As a result, the final feature vector contains ROI and background information in their proper proportion to improve retrieval performance. Moreover, the performance of the proposed CBIR scheme has been evaluated using one medical, three natural, two objects, one texture, and one natural heterogeneous image database. All these databases collectively contain 53732 different images from 200 image classes. The outcomes of the experiments demonstrate substantial improvement in the retrieval process with some existing related schemes. At the same time, it also shows that the performance of the proposed scheme is robust concerning all kinds of image databases.

A hierarchical approach based CBIR scheme using shape, texture, and color for accelerating retrieval process

In the traditional content-based image retrieval (CBIR) framework, images are retrieved based on the combined primitive features. However, such a kind of fusion is always not effective as one feature may overshadow other image feature. To overcome this issue, in this particular paper, we have suggested a hierarchical framework where features like color, texture, and shape are considered in a single hierarchy only. The main concern of the hierarchical system is the proper selection of the order of retrieving visual features. So, semantic-based image retrieval has been carried out. Here, at the first level, the salience map-based region of interest has been identified, and then edge histogram descriptor-based shape features are incorporated. In the second hierarchy, we have proposed a novel directional texture feature extraction based on the Tamura features’ directionality. Further, color is considered another primitive feature of an image, but human visual perception is not sensitive to each color. The image can be visualized by the salient colors, and in this work, we have developed a color image quantization-based approach. Now, to validate the system, extensive experimental results and its comparison with its contemporaries through Corel-1 K, GHIM-10 K, Olivia-2688, and Produce-1400 databases have been carried out.

VFIRM: Verifiable Fine-Grained Encrypted Image Retrieval in Multi-Owner Multi-User Settings

To ensure the security of images outsourced to the malicious cloud without affecting searchability on such outsourced (typically encrypted) images, one could use privacy-preserving Content-Based Image Retrieval (CBIR) primitive. However, conventional privacy-preserving CBIR schemes based on Searchable Symmetric Encryption (SSE) are not capable of supporting efficient fine-grained access control and result verification simultaneously. Therefore, in this article, we propose a <u>V</u> erifiable <u>F</u> ine-grained encrypted <u>I</u> mage <u>R</u> etrieval scheme in the <u>M</u> ulti-owner multi-user settings (VFIRM). VFIRM first utilizes a novel polynomial-based access strategy to provide efficient fine-grained access control. Then, it employs the dual secure <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -nearest neighbor technique to distribute distinct keys to different data owners and data users, and finally implements an adapted homomorphic MAC technique to check the correctness of search results. Our formal security analysis shows that VFIRM is non-adaptive semantic secure if the client's search key is generated randomly and keeps in secret. Our empirical experiments using two real-world datasets (i.e., Caltech101 and Corel5k) demonstrate the practicality of VFIRM.

Privacy-Preserve Content-Based Image Retrieval Using Aggregated Local Features

Due to the recent improvements in imaging and computing technologies, a massive quantity of image data is generated every day. For searching image collection, several content-based image retrieval (CBIR) methods have been introduced. However, these methods need more computing and storage resources. Cloud servers can fill this gap by providing huge computational power at a cheap price. However, cloud servers are not fully trusted, thus image owners have legal concerns about the privacy of their private data. In this paper, we proposed and implemented a privacy-preserving CBIR (PP-CBIR) scheme that allows searching and retrieving image databases in a cipher text format. Specifically, we extract aggregated feature vectors to represent the corresponding image collection and employ the asymmetric scalar-product-preserving encryption scheme (ASPE) method to protect these vectors while allowing for similarity computation between these encrypted vectors. To enhance search time, all encrypted features are clustered by the k-means algorithm recursively to construct a tree index. Results show that PP-CBIR has faster indexing and retrieving with good retrieval precision and scalability than previous schemes.

A Format-compatible Searchable Encryption Scheme for JPEG Images Using Bag-of-words

The development of cloud computing attracts enterprises and individuals to outsource their data, such as images, to the cloud server. However, direct outsourcing causes the extensive concern of privacy leakage, as images often contain rich sensitive information. A straightforward way to protect privacy is to encrypt the images using the standard cryptographic tools before outsourcing. However, in such a way the possible usage of the outsourced images would be strongly limited together with the services provided to users, like the Content-Based Image Retrieval (CBIR). In this article, we propose a secure outsourced CBIR scheme, in which an encryption scheme is designed for the widely used JPEG-format images, and the secure features can be directly extracted from such encrypted images. Specifically, the JPEG images are encrypted by the block permutation, intra-block permutation, polyalphabetic cipher, and stream cipher. Then secure local histograms are extracted from the encrypted DCT blocks and the Bag-Of-Words (BOW) model is further used to organize the encrypted local features to represent the image. The proposed image encryption gets all of the image data protected and the experimental results show that the proposed scheme achieves improved accuracy with a small file size expansion.

A content-based image retrieval scheme with object detection and quantised colour histogram

Content-based image retrieval (CBIR) is an active area of research due to its wide applications. Most of the existing CBIR schemes are concentrated to do the searching of the images based on the texture, colour, or shape features extracted from the query image. In this manuscript, we propose an object detection-based CBIR scheme with quantised colour histograms. In the proposed scheme, the meaningful objects will be identified from the query image by using you only look once (YOLO) object detection techniques and the quantised histograms of each of the object categories. The object lists, their count, and the area covered by the objects along with quantised colour histograms will be used during feature matching to retrieve the related images from the large image pool. The experiment of the proposed scheme is carried on the Corel 1K and Caltech image dataset. We have observed an average precision of 0.96 during the experimental study which is quite high while comparing the precision from the well-known existing schemes.

A content-based image retrieval scheme with object detection and quantised colour histogram

A content-based image retrieval scheme with object detection and quantised colour histogram

BOEW: A Content-Based Image Retrieval Scheme Using Bag-of-Encrypted-Words in Cloud Computing

Content-based Image Retrieval (CBIR) techniques have been extensively studied with the rapid growth of digital images. Generally, CBIR service is quite expensive in computational and storage resources. Thus, it is a good choice to outsource CBIR service to the cloud server that is equipped with enormous resources. However, the privacy protection becomes a big problem, as the cloud server cannot be fully trusted. In this paper, we propose an outsourced CBIR scheme based on a novel bag-of-encrypted-words (BOEW) model. The image is encrypted by color value substitution, block permutation, and intra-block pixel permutation. Then, the local histograms are calculated from the encrypted image blocks by the cloud server. All the local histograms are clustered together, and the cluster centers are used as the encrypted visual words. In this way, the bag-of-encrypted-words (BOEW) model is built to represent each image by a feature vector, i.e., a normalized histogram of the encrypted visual words. The similarity between images can be directly measured by the Manhattan distance between feature vectors on the cloud server side. Experimental results and security analysis on the proposed scheme demonstrate its search accuracy and security.

Modified cuckoo search algorithm: Feature subset selection &amp;Shape,Color and Texture Features Descriptors for Content-Based Image Retrieval

With the recent advances in knowledge, the complication of multimedia has increased expressively and new areas of research have opened up in search of new multimedia content. Content-based image retrieval (CBIR) are used to extract images associated with image queries (IQs) from huge databases. The CBIR schemes accessible at present have limited functionality because they only have a partial number of functions. This document presents an improved cookie detection algorithm with coarse sentences for processing large amounts of data using selected examples. The improved cuckoo detection algorithm mimics the behavior of brood attachment parasites in some cuckoo species, including some birds. Modified cuckoo recognition uses approximate set theory to create a fitness function that takes into account the sum of features and the quality of classification as a small amount. For an image entered as IQ from a database, distance metrics are used to find the appropriate image. This is the central idea of CBIR. The projected CBIR method is labelled and can extract shape features based on the RGB color using the and canny Edge (CED) and neutrosophic clustering algorithm scheme. After YCbCrcolor cut, and the CED to get the features to extract the vascular matrix. The combination of these techniques improves the efficiency of the CBR image recovery infrastructure. In this thesis recursive neural network techniques are used to measure the similarity. In addition, the accuracy of the results is: The recall score is measured to evaluate system performance. The proposed CBIR system provides more precise and accurate values than the complex CBIR system.

A CBIR system based on saliency driven local image features and multi orientation texture features

In Content-based Image Retrieval (CBIR), the user provides the query image in which only a selective portion of the image carries the foremost vital information known as the object region of the image. However, the human visual system also focuses on a particular salient region of an image to instinctively understand its semantic meaning. Therefore, the human visual attention technique can be well imposed in the CBIR scheme. Inspired by these facts, we initially utilized the signature saliency map-based approach to decompose the image into its respective main object region (ObR) and non-object region (NObR). ObR possesses most of the vital image information, so block-level normalized singular value decomposition (SVD) has been used to extract salient features of the ObR. In most natural images, NObR plays a significant role in understanding the actual semantic meaning of the image. Accordingly, multi-directional texture features have been extracted from NObR using Gabor filter on different wavelengths. Since the importance of ObR and NObR features are not equal, a new homogeneity-based similarity matching approach has been devised to enhance retrieval accuracy. Finally, we have demonstrated retrieval performances using both the combined and distinct ObR and NObR features on seven standard coral, texture, object, and heterogeneous datasets. The experimental outcomes show that the proposed CBIR system has a promising retrieval efficiency and outperforms various existing systems substantially.

A faster secure content-based image retrieval using clustering for cloud

Secure indexing, ranking, searching, and retrieval in encrypted domain without compromising privacy is a challenging task. This is even more difficult in the case of images. In this paper, we propose a faster, more efficient, and highly scalable secure content-based image retrieval scheme using clustering for cloud. In the proposed scheme, the image owner extracts MPEG-7 visual descriptors from all the images and clusters them for indexing. These image features and cluster centers encrypted using Asymmetric scalar-product preserving encryption are offloaded to the cloud along with encrypted images to ensure secure retrieval of images. We also propose a copy-deterrence mechanism to identify untrustworthy query users. The proposed scheme is highly scalable, up to seven times faster in searching and up to 23 times faster in indexing with better retrieval precision compared with the fastest state-of-the-art scheme. The proposed copy deterrence mechanism ensures better speed and perceptual quality of watermarked images with better or comparable extraction accuracy when compromised.

Computational intelligence based secure three-party CBIR scheme for medical data for cloud-assisted healthcare applications

Medical images with various modalities have become an integral part of the diagnosis and treatment of several diseases. The medical practitioners often use previous case studies to deal with the current medical condition of any particular patient. In such circumstance, they need to securely access medical images of various cases which are generally stored in a network and are vulnerable to malicious attacks. To address these sensitive inadequacies, we have proposed computational intelligence based secure healthcare Content based Image Retrieval (CBIR) for medical image retrieval scheme through which any medical practitioner can retrieve the image in an encrypted domain in cloud environment. In this regard, hamming distance-based similarity matching is the only available technique that effectively handles the comparison between encrypted features. This technique requires binary features to perform similarity matching, and the performance of such features in image retrieval is poor. In this concern, we have suggested a salient component-based binary feature extraction approach to enhance retrieval accuracy. Initially, we have re-arranged the input image using the saliency map, principal texture direction, and entropy to place the salient components at the starting blocks. Subsequently, we have employed a block-level majority voting scheme on the salient blocks of the image to obtain local binary features. As a result, the final feature vector carries more features from the salient part of the image, which propitiously improves the retrieval accuracy. Later, we have encrypted the binary feature vector and performed image retrieval on cloud environment which involve Data Owner, Database Service Provider and Client over encrypted domain to full fill the security aspect. Finally, we have used medical as well as Corel image datasets to validate the retrieval performance accuracy of the proposed scheme. The experimental results obtained from real life datasets exhibit that the proposed method is secure and provides comparable retrieval accuracy concerning other related schemes in the domain.

A privacy-preserving and traitor tracking content-based image retrieval scheme in cloud computing

To ensure the image security, a large number of ciphertext image retrieval methods have been studied and applied, such as homomorphic encryption and multi-key encryption. However, most of these algorithms do not consider the protection of image copyright, user information and traitor tracking. For this reason, this paper proposes a privacy-preserving and traitor tracking content-based image retrieval scheme in cloud computing. This method introduces the DenseNet network to strengthen statistical features. One-way hash algorithm and XOR operation are used to protect copyright and user information, and reversible information hiding algorithm is accessed for traitor tracking. Experimental results show that compared with other algorithms that support leak tracking, our method achieves higher retrieval precision, higher retrieval efficiency and simpler architecture.

OPPR: An Outsourcing Privacy-Preserving JPEG Image Retrieval Scheme with Local Histograms in Cloud Environment

As the wide application of imaging technology, the number of big image data which may containing private information is growing fast. Due to insufficient computing power and storage space for local server device, many people hand over these images to cloud servers for management. But actually, it is unsafe to store the images to the cloud, so encryption becomes a necessary step before uploading to reduce the risk of privacy leakage. However, it is not conducive to the efficient application of image, especially in the Content-Based Image Retrieval (CBIR) scheme. This paper proposes an outsourcing privacypreserving JPEG CBIR scheme. We design a set of JPEG format-compatible encryption method, making no file expansion to JPEG files. We firstly combine multiple adjacent 8 × 8 DCT coefficient blocks into big-blocks. Then, random scrambling and stream encryption are used on the binary code of DCT coefficients to protect the JPEG image privacy. The task of extracting features from encrypted images and retrieving similar images are done by the cloud server. The group index histograms of DCT coefficients are extracted from the encrypted big-blocks, then the global vector is produced to represent the JPEG image with the aid of bagof-words (BOW) model. The security analysis and experimental results show that our proposed scheme has strong security and good retrieval performance.

Fusion of region based extracted features for instance- and class-based CBIR applications

One of the major challenges in content-based image retrieval (CBIR) is understanding the actual semantic meaning of images. Generally, natural images possess a rich collection of different visual features, each of which have different contributions to the image semantics. These vary drastically in their local sub-regions. However, most of the state-of-the-art CBIR techniques give equal importance to all the visual features, which are not always effective for generating relevant retrieval outcomes. To overcome these issues, a CBIR scheme has been proposed which considers the image semantic properties of the different regions with their positional significance in the overall image. In this paper, a novel regions-of-attention (ROA) based feature extraction and fusion scheme for efficient image retrieval is suggested. Initially, a salient key-points and opponent-color feature based scheme is used to locate ROA of the image. Multi-directional texture, and spatial correlation-based color features are extracted from this ROA of the image to obtain most of the image’s semantic meanings. Spatial features from salient non-ROA local regions are also obtained to capture vital background information. A dissimilarity factor-based fusion scheme then is utilized to fuse the ROA and non-ROA features effectively, considering the importance and uniqueness of each feature to support the positional invariance notion in their respective sub-regions. Finally, the fused feature vector is used to perform instance- and class-based image retrieval in five different image datasets using different classifiers. The outcomes of the retrieval experiment have shown competitive performances as compared to the current state-of-the-art architectures.

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.

Similarity Search for Encrypted Images in Secure Cloud Computing

With the emergence of intelligent terminals, the Content-Based Image Retrieval (CBIR) technique has attracted much attention from many areas (i.e., cloud computing, social networking services, etc.). Although existing privacy-preserving CBIR schemes can guarantee image privacy while supporting image retrieval, these schemes still have inherent defects (i.e., low search accuracy, low search efficiency, key leakage, etc.). To address these challenging issues, in this article we provide a similarity Search for Encrypted Images in secure cloud computing (called SEI). First, the feature descriptors extracted by the Convolutional Neural Network (CNN) model are used to improve search accuracy. Next, an encrypted hierarchical index tree by using <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -means clustering based on Affinity Propagation (AP) clustering is devised, which can improve search efficiency. Then, a limited key-leakage k-Nearest Neighbor (kNN) algorithm is proposed to protect key from being completely leaked to untrusted image users. Finally, SEI is extended to further prevent image users’ search information from being exposed to the cloud server. Our formal security analysis proves that SEI can protect image privacy as well as key privacy. Our empirical experiments using a real-world dataset illustrate the higher search accuracy and efficiency of SEI.

Deep Learning Using Symmetry, FAST Scores, Shape-Based Filtering and Spatial Mapping Integrated with CNN for Large Scale Image Retrieval

This article presents symmetry of sampling, scoring, scaling, filtering and suppression over deep convolutional neural networks in combination with a novel content-based image retrieval scheme to retrieve highly accurate results. For this, fusion of ResNet generated signatures is performed with the innovative image features. In the first step, symmetric sampling is performed on the images from the neighborhood key points. Thereafter, the rotated sampling patterns and pairwise comparisons are performed, which return image smoothing by applying standard deviation. These values of smoothed intensity are calculated as per local gradients. Box filtering adjusts the results of approximation of Gaussian with standard deviation to the lowest scale and suppressed by non-maximal technique. The resulting feature sets are scaled at various levels with parameterized smoothened images. The principal component analysis (PCA) reduced feature vectors are combined with the ResNet generated feature. Spatial color coordinates are integrated with convolutional neural network (CNN) extracted features to comprehensively represent the color channels. The proposed method is experimentally applied on challenging datasets including Cifar-100 (10), Cifar-10 (10), ALOT (250), Corel-10000 (10), Corel-1000 (10) and Fashion (15). The presented method shows remarkable results on texture datasets ALOT with 250 categories and fashion (15). The proposed method reports significant results on Cifar-10 and Cifar-100 benchmarks. Moreover, outstanding results are obtained for the Corel-1000 dataset in comparison with state-of-the-art methods.

CONTENT-BASED IMAGE RETRIEVAL FOR MOBILE DEVICES USING MULTI-STAGE AUTOENCODERS

Content-based image retrieval (CBIR) composes of retrieving the most similar images from an online or offline database concerning a query image. In this article, we proposed a CBIR technique which uses a multi-level stacked Autoencoders for feature selection and dimensionality reduction. The present work more focus towards an image retrieval approach on a mobile device such as mobile phones and tablets. The approach uses feature descriptors derived from a stacked autoencoder where both the abstract feature extraction and the dimensionality reduction are handled simultaneously in the autoencoder stages. A query image space is created first before the actual retrieval process by combining the query image as well as similar images from the local image database (images in device gallery) to maintain the image saliency in the visual contents. The features corresponding to the query image space elements are searched against the characteristics of images in a global dataset (target images from cloud space, servers, etc.) in a weighted manner, and the target images are ranked based on the similarity scores. The top ranked images from the global dataset is then selected based on the thresholding of similarity score. The proposed CBIR scheme is experimented over standard public datasets, and performance is compared against state-of-the-art image retrieval approaches. The results show significant improvements in the overall precision, recall and time cost and justifies the image search over mobile devices with less computation capability.