Image Database Retrieval Research Articles

Retracted: Research on Clothing Image Database Retrieval Algorithm Based on Wavelet Transform

Retracted: Research on Clothing Image Database Retrieval Algorithm Based on Wavelet Transform

Locality-Sensitive Hashing of Soft Biometrics for Efficient Face Image Database Search and Retrieval

As multimedia technology has advanced in recent years, the use of enormous image libraries has dramatically expanded. In applications for image processing, image retrieval has emerged as a crucial technique. Content-based face image retrieval is a well-established technology in many real-world applications, such as social media, where dependable retrieval capabilities are required to enable quick search among large numbers of images. Humans frequently use faces to recognize and identify individuals. Face recognition from official or personal photos is becoming increasingly popular as it can aid crime detectives in identifying victims and criminals. Furthermore, a large number of images requires a large amount of storage, and the process of image comparison and matching, consequently, takes longer. Hence, the query speed and low storage consumption of hash-based image retrieval techniques have garnered a considerable amount of interest. The main contribution of this work is to try to overcome the challenge of performance improvement in image retrieval by using locality-sensitive hashing (LSH) for retrieving top-matched face images from large-scale databases. We use face soft biometrics as a search input and propose an effective LSH-based method to replace standard face soft biometrics with their corresponding hash codes for searching a large-scale face database and retrieving the top-k of the matching face images with higher accuracy in less time. The experimental results, using the Labeled Faces in the Wild (LFW) database together with the corresponding database of attributes (LFW-attributes), show that our proposed method using LSH face soft biometrics (Soft BioHash) improves the performance of face image database search and retrieval and also outperforms the LSH hard face biometrics method (Hard BioHash).

SYMBIOSIS OF MEDICAL TECHNOLOGIES AND ARTIFICIAL INTELLIGENCE: NEW OPPORTUNITIES IN ONCOLOGY

SYMBIOSIS OF MEDICAL TECHNOLOGIES AND ARTIFICIAL INTELLIGENCE: NEW OPPORTUNITIES IN ONCOLOGY

Research on the Application of Artificial Intelligence in Image and Text Database Retrieval

In the graphic database, the difficulty of query processing lies in how to query the contents of various data, that is, content-based retrieval, which is an effective means and an important technology to realize multimedia data retrieval. In graphic database, the difficulty of query processing lies in how to query the content based on unformatted data, that is, content-based retrieval, which is an effective means and an important technology to realize multimedia data retrieval. Intelligent information retrieval (IR) system is an intelligent computer IR system, which simulates the thinking process and intelligent activities of human beings about information processing, realizes the storage, retrieval and reasoning of information knowledge, and provides intelligent assistance to users. This paper analyzes the problems of image and text database retrieval driven by big data, explores the application effect of artificial intelligence (AI) in IR driven by big data, promotes the innovation and transformation of modern science and technology, and realizes the sustainable development of our society.

FSeCBIR: A Faster Secure Content-Based Image Retrieval for Cloud

<h2>Abstract</h2> In recent years, content-based image retrieval in encrypted domain has emerged as an important challenge in cloud environment. We present FSeCBIR, a prototype that implements Faster and Secure Content-Based Image Retrieval ensuring secure search and retrieval in large encrypted image databases. This prototype is implemented using MATLAB on a server/high-end PC and it incorporates a copy deterrence module to identify data leaks through untrustworthy query users. This simulates a system that can be used in real-time cloud applications involving sensitive information which demand high level of security, retrieval precision, and less time/space complexity.

Retracted] Research on Clothing Image Database Retrieval Algorithm Based on Wavelet Transform

Aiming at the problems of low image data retrieval accuracy and slow retrieval speed in the existing image database retrieval algorithms, this paper designs a clothing image database retrieval algorithm based on wavelet transform. Firstly, it represents the color consistency vector of clothing image, reflects the composition and distribution of image color through color histogram, quantifies the visual features of clothing image, aggregates them into a fixed size representation vector, and uses the Fair Value (FV) model to complete the collection of clothing image data. Then, the size of the clothing image is adjusted by using the size transformation technology, and the clothing pattern is divided into four moments with the same size. On this basis, the clothing image is discretized with the help of Hu invariant moment to complete the preprocessing of clothing image data. Finally, the generating function of wavelet transform is determined, and a cluster of functions is obtained through translation and expansion. The wavelet filter is decomposed into basic modules, and then, the wavelet transform is studied step by step. The clothing image data are regarded as a signal, split, predicted, and updated and input into the wavelet model, and the retrieval research of clothing image database is completed. The experimental results show that the design of the retrieval algorithm is reasonable, the retrieval data accuracy is high, and the retrieval speed is fast.

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.

Effect of image partitioning on content-based image retrieval using colour and texture

The current application of content-based image retrieval (CBIR) was urgently needed to process image retrieval in large image databases. Previous research had succeeded in combining feature extraction methods using texture and colour. Combining the two methods of Gray Level Co-Occurrence matrix with Colour moment produces a higher level of precision than that produced when method is applied individually. This research examined more deeply about the effect of image partitioning using the grid partitioning method. The results of precision and recall on the CBIR method would be compared with no partitions, two partitions and three image partitions. The results obtained indicated that the addition of image partitions did not affect the precision value. Thus, it can be concluded that increasing the image partition increases the recall value of the overall CBIR performance.

Research on Discrete Hash Algorithm Based on Deep Semantics

With the rapid growth of image and video data on the network, hash technology has been widely studied in the field of image and video search in recent years. Benefiting from the latest progress in deep learning, deep hash method has achieved good results in image retrieval. However, the previous deep hash method has the limitation that the semantic information is not fully utilized. In this paper, we develop a discrete hash algorithm based on deep supervision, assuming that learning binary code should be an ideal choice of classification. The pair tag information and classified information are used to learn hash codes within a framework. The output of the last layer is restricted to binary code directly, which is rarely studied in deep hash algorithm. Due to the discrete properties of hash codes, the alternate minimization method is used to optimize the target function. The proposed algorithm is proved to be better than the other supervised hash methods in two public image retrieval databases CIFAR-10 and NUS-WIDE.

A novel image retrieval algorithm based on transfer learning and fusion features

With proliferation of social media, image has become ubiquitous giving rise to the demand and importance of image semantic analysis and retrieval to access information quickly on social media. However, even with humongous information available, there are certain categories of images which are important for certain applications but are very scarce. Convolutional neural network is an effective method to extract high-level semantic features for image database retrieval. To overcome the problem of over-fitting when the number of training samples in dataset is limited, this paper proposes an image database retrieval algorithm based on the framework of transfer learning and feature fusion. Based on the fine-tuning of the pre-trained Convolutional Neural Network (CNN), the proposed algorithm first extracts the semantic features of the images. Principal Component Analysis (PCA) is then applied for dimension reduction and to reduce the computational complexity. Last, the semantic feature extracted from the CNN is fused with traditional low-level visual feature to improve the retrieval accuracy further. Experimental results demonstrated the effectiveness of the proposed method for image database retrieval.

Asymmetric Binary Coding for Image Search

Learning to hash has attracted broad research interests in recent computer vision and machine learning studies, due to its ability to accomplish efficient approximate nearest neighbor search. However, the closely related task, maximum inner product search (MIPS), has rarely been studied in this literature. To facilitate the MIPS study, in this paper, we introduce a general binary coding framework based on asymmetric hash functions, named asymmetric inner-product binary coding (AIBC). In particular, AIBC learns two different hash functions, which can reveal the inner products between original data vectors by the generated binary vectors. Although conceptually simple, the associated optimization is very challenging due to the highly nonsmooth nature of the objective that involves sign functions. We tackle the nonsmooth optimization in an alternating manner, by which each single coding function is optimized in an efficient discrete manner. We also simplify the objective by discarding the quadratic regularization term which significantly boosts the learning efficiency. Both problems are optimized in an effective discrete way without continuous relaxations, which produces high-quality hash codes. In addition, we extend the AIBC approach to the supervised hashing scenario, where the inner products of learned binary codes are forced to fit the supervised similarities. Extensive experiments on several benchmark image retrieval databases validate the superiority of the AIBC approaches over many recently proposed hashing algorithms.

Location-Based Parallel Tag Completion for Geo-Tagged Social Image Retrieval

Having benefited from tremendous growth of user-generated content, social annotated tags get higher importance in the organization and retrieval of large-scale image databases on Online Sharing Websites (OSW). To obtain high-quality tags from existing community contributed tags with missing information and noise, tag-based annotation or recommendation methods have been proposed for performance promotion of tag prediction. While images from OSW contain rich social attributes, they have not taken full advantage of rich social attributes and auxiliary information associated with social images to construct global information completion models. In this article, beyond the image-tag relation, we take full advantage of the ubiquitous GPS locations and image-user relationship to enhance the accuracy of tag prediction and improve the computational efficiency. For GPS locations, we define the popular geo-locations where people tend to take more images as Points of Interests (POI), which are discovered by mean shift approach. For image-user relationship, we integrate a localized prior constraint, expecting the completed tag sub-matrix in each POI to maintain consistency with users’ tagging behaviors. Based on these two key issues, we propose a unified tag matrix completion framework, which learns the image-tag relation within each POI. To solve the optimization problem, an efficient proximal sub-gradient descent algorithm is designed. The model optimization can be easily parallelized and distributed to learn the tag sub-matrix for each POI. Extensive experimental results reveal that the learned tag sub-matrix of each POI reflects the major trend of users’ tagging results with respect to different POIs and users, and the parallel learning process provides strong support for processing large-scale online image databases. To fit the response time requirement and storage limitations of Tag-based Image Retrieval (TBIR) on mobile devices, we introduce Asymmetric Locality Sensitive Hashing (ALSH) to reduce the time cost and meanwhile improve the efficiency of retrieval.

BHCR: RSVP target retrieval BCI framework coupling with CNN by a Bayesian method

To combine the complementary strengths of human vision (HV) and computer vision (CV) in target image retrieval, we proposed a brain-computer interface framework, Bayesian HV-CV Retrieval (BHCR), which couples HV with CV by a Bayesian method to retrieve target images in rapid serial visual presentation (RSVP) sequences. To construct a well-suited electroencephalogram (EEG) decoding module for BHCR, we conducted a comparative inspection on the selection of classification algorithms, and adopted linear discriminant analysis and random forests as a feature extraction method and classification algorithm, respectively. We also introduced a CV system based on convolutional neural network (CNN) as a component of BHCR. A Bayesian brain-computer interaction (BBCI) module was carefully designed so that for each presented image, a Bayesian model that takes HV insight as prior information and CV insights as sample information is built up to present retrieval results. Unlike existing HV-CV coupled works that usually require extra manual labor, BHCR directly enhanced retrieval performance with the help of CV insights. As an auxiliary work and a natural extension of BHCR, we then proposed a probability propagation scheme that incorporates EEG decoding insights to improve the CV system and a one-shot image database retrieval scheme. We demonstrated the effectiveness of BHCR by extensive experiments and simulations on both the entire framework and its sub-components. The results showed the following: (1) The performance of BHCR was significantly better than the EEG-only mechanism in both receiver operating characteristic (ROC) and classification aspects; (2) The robustness of BHCR was ensured by its process flow and the steady performances of its sub-components.

Image database of printed fabric with repeating dot patterns part (I) – image archiving

An image database of printed fabrics with repeating dot patterns was created to alleviate issues associated with management of and searches for numerous dot printed fabrics in the printing industry. The function of the database is to archive and allow retrieval of images. First, we discuss image archiving of repeating pattern-based dot printed fabrics. The color image was scanned by resolution of 200 dpi. The wavelet transformation was used to preprocess the image to obtain a scanned image 1/16 of the size of the original to be the stored image. To acquire images with repeating pattern color and repeating pattern template, the binary image of each pattern was obtained using the Sobel edge detection method and a morphological operation. Then pattern elements identical to the target pattern element were screened out. Afterwards, the centroid positions of these identical pattern elements were used to subdivide the repeating pattern color image and repeating pattern template image using a vertical vector method. Finally, the RGB 512-color histogram was used as the color feature of the dot printed fabrics, and the geometric and moment invariant feature values of the repeating pattern template image were used as the pattern feature of the dot printed fabrics. Our experimental results show that images can be acquired that are suitable for use in a dot printed fabric image database. The color and template images of the repeating patterns, which represent the image content of the printed fabrics, were obtained to create an image database of repeating pattern-based dot printed fabrics. This image database contains data on 300 printed fabrics which can be used for subsequent research on database image retrieval.

Study on rotation-invariant texture feature extraction for tire pattern retrieval

One key task in forensic science is to perform criminal investigation through image database retrieval. Of the various images, tire pattern is an important type of image data for crime scene investigation. However, different rotation and direction of tire patterns are often encountered and is insufficient to use the conventional multi-scale texture feature extraction method which is not rotational invariant. To alleviate this problem, the paper proposed two new texture feature extraction methods based on the Radon transform and Curvelet transform. The experiments were conducted using a tire pattern database containing 400 images. The results show that the proposed methods effectively overcome the influences of rotation and significantly improve the retrieval efficiency.

A conceptual framework for a computer-assisted, morphometric-based phytolith analysis and classification system

Although automated approaches to shape analysis and object classification have been widely applied in the biological sciences, technical and time considerations have limited their use in phytolith research. As advanced microscopy systems become more affordable and accessible and digital imaging software provides a wider range of sophisticated analytical tools, there is increased potential for effective use of machine-vision and automation in phytolith research. In this paper, we describe technical limitations of phytolith imaging and identify several techniques that might improve results. Drawing on examples of software developed for related disciplines, we then describe a conceptual framework for development and integration of automated phytolith analysis software for: separating phytoliths from non-phytolith material in digital images; segmentation of phytolith boundaries; quantitative phytolith feature extraction, including a discussion of potentially more powerful, non-traditional parameters of phytolith shape and texture; phytolith classification and identification; and phytolith database image retrieval. While recognizing the difficulty of implementing this framework and the need for extensive empirical testing of suggested approaches on phytoliths, we examine the possibility of aggregating quantitative phytolith data collected in studies worldwide to construct a cloud-based database of phytolith images with associated morphotype data.

Research and Application of Image Retrieval Improved Algorithm Based on BOF

The feature description of image and the index mechanism of the feature are the keys to realize the content-based image retrieval, aiming at the problem of massive image data and “dimension curse”, this paper proposes the BOF-based image retrieval improved algorithm, and combines with VLAD and soft assignment it generates the soft assignment local aggregation descriptor (SA-VLAD) which has a better ability to resist the dimension reduction and a higher recognition rate. When the index mechanism IVFADC is at query time, to ensure the recall ratio and precision rate of the result, the candidates inverted index chain are increased, which leads to the problems of distance calculation and the query time’s increasing. For this point, in the index phase, the scattered distribution is carried out aiming at the database vector, which reduces the burden of distance calculation, and improves the quality of the query results at the same time. The experimental results show that the algorithm in this paper obtains a good effect in the content-based massive image database retrieval.

Sketch retrieval based on qualitative shape similarity matching: Towards a tool for teaching geometry to children

An approach for a query-by-sketch system on qualitative shape information for image retrieval in databases is proposed and evaluated. The use of qualitative methods for shape description allows the gathering of semantic information from the sketches. The qualitative description and recognition of sketches are evaluated in order to verify that it is possible to use the proposed qualitative method for the development of a learning application for children.

Towards an interactive index structuring system for content-based image retrieval in large image databases

In recent years, the expansion of acquisition devices such as digital cameras, the development of storage and transmission techniques and the success of tablet computers facilitate the development of many large image databases as well as the interactions with the users. This thesis [1] deals with the problem of Content-Based Image Retrieval (CBIR) on these huge masses of data. Traditional CBIR systems generally rely on three phases: feature extraction, feature space structuring and retrieval. In this thesis, we are particularly interested in the structuring phase (normally called indexing phase), which plays a very important role in finding information in large databases. This phase aims at organizing the visual feature descriptors of all images into an efficient data structure in order to facilitate, accelerate and improve further retrieval. We assume that the feature extraction phase is completed and the image feature descriptors which are usually low-level features describing the color, shape, texture, etc. of all images are available. Instead of traditional structuring methods, clustering methods which organize image descriptors into groups of similar objects (clusters), without any constraint on the cluster size, are studied. The aim is to obtain an indexed structure more adapted to the retrieval of high dimensional and unbalanced data. Clustering process can be done without prior knowledge (unsupervised clustering) or with a limited amount of prior knowledge (semi-supervised clustering).

Unsupervised and interactive semi-supervised clustering for large image database indexing and retrieval.

Unsupervised and interactive semi-supervised clustering for large image database indexing and retrieval.