Fusion of Hash-Based Hard and Soft Biometrics for Enhancing Face Image Database Search and Retrieval

The utilization of digital picture search and retrieval has grown substantially in numerous fields for different purposes during the last decade,owing to the continuing advances in image processing and computer vision approaches.In multiple real-life applications,for example,social media,content-based face picture retrieval is a well-invested technique for large-scale databases,where there is a significant necessity for reliable retrieval capabilities enabling quick search in a vast number of pictures.Humans widely employ faces for recognizing and identifying people.Thus,face recognition through formal or personal pictures is increasingly used in various real-life applications,such as helping crime investigators retrieve matching images from face image databases to identify victims and criminals.However,such face image retrieval becomes more challenging in large-scale databases,where traditional vision-based face analysis requires ample additional storage space than the raw face images already occupied to store extracted lengthy feature vectors and takes much longer to process and match thousands of face images.This work mainly contributes to enhancing face image retrieval performance in large-scale databases using hash codes inferred by locality-sensitive hashing(LSH)for facial hard and soft biometrics as(Hard BioHash)and(Soft BioHash),respectively,to be used as a search input for retrieving the top-k matching faces.Moreover,we propose the multi-biometric score-level fusion of both face hard and soft BioHashes(Hard-Soft BioHash Fusion)for further augmented face image retrieval.The experimental outcomes applied on the Labeled Faces in the Wild(LFW)dataset and the related attributes dataset(LFW-attributes),demonstrate that the retrieval performance of the suggested fusion approach(Hard-Soft BioHash Fusion)significantly improved the retrieval performance compared to solely using Hard BioHash or Soft BioHash in isolation,where the suggested method provides an augmented accuracy of 87%when executed on 1000 specimens and 77%on 5743 samples.These results remarkably outperform the results of the Hard BioHash method by(50%on the 1000 samples and 30%on the 5743 samples),and the Soft BioHash method by(78%on the 1000 samples and 63%on the 5743 samples).

Research on Image Preprocessing Algorithm for Rail Surface Recognition

The rail surface status image is affected by the noise in the shooting environment and contains a large amount of interference information, which increases the difficulty of rail surface status identification. In order to solve this problem, a preprocessing method for the rail surface state image is proposed. The preprocessing process mainly includes image graying, image denoising, image geometric correction, image extraction, data amplification, and finally building the rail surface image database. The experimental results show that this method can efficiently complete image processing, facilitate feature extraction of rail surface status images, and improve rail surface status recognition accuracy.

Performance Validation and Analysis for Multi-Method Fusion Based Image Quality Metrics in A New Image Database

Considering that there is no single full reference image quality assessment method that could give the best performance in all situations, some multi-method fusion metrics were proposed. Machine learning techniques are often involved in such multi-method fusion metrics so that its output would be more consistent with human visual perceptions. On the other hand, the robustness and generalization ability of these multi-method fusion metrics are questioned because of the scarce of images with mean opinion scores. In order to comprehensively validate whether or not the generalization ability of such multi-method fusion IQA metrics are satisfying, we construct a new image database which contains up to 60 reference images. The newly built image database is then used to test the generalization ability of different multi-method fusion IQA metrics. Cross database validation experiment indicates that in our new image database, the performances of all the multi-method fusion IQA metrics have no statistical significant different with some single-method IQA metrics such as FSIM and MAD. In the end, a thorough analysis is given to explain why the performance of multi-method fusion IQA framework drop significantly in cross database validation.

Methods of 3D map storage based on geo-referenced image database

In order to store and manage a large amount of ground and indoor image data with high resolution, an integrated data management system needs to be developed. Possible strategies for this purpose were discussed together with initial test on the newly defined 3D maps. The features of such 3D maps, data organization, key techniques used for the map storage, such as image compression based on wavelet transformation, quadtree index, update and retrieval, were analyzed, with the goals of bringing some profits to the storage and management of the digital data in the visual construction of digital mine, digital city and digital community.

Design of Content-Based Retrieval System in Remote Sensing Image Database

To retrieve the object region efficaciously from massive remote sensing image database, a model for content-based retrieval of remote sensing image is given according to the characters of remote sensing image application firstly, and then the algorithm adopted for feature extraction and multidimensional indexing, and relevance feedback by this model are analyzed in detail. Finally, the contents intending to be researched about this model are proposed.

IMAGE DATABASE FOR PATTERN DESIGN SYSTEM

This paper discusses a pattern design system in textile industry,the establishment of an imagedatabase which is used to store various kinds of source materials for designers’ reference in order tospeed up design process.Pattern design image database (PDIDB) runs on the double-machine hardware system com-posed of ALTOS-986 and IBM PC/XT microcomputer.The former (host) manages imagedatabase,and the latter works both as a terminal to operate PDIDB and as an image processingstation to input,output,edit and display image data.PDIDB has two mainparts,the image storage management system and the image attributemanagement system and provides some functions,such as retrieval,deleting and updating.

CMA:an efficient index algorithmof clustering supporting fast retrieval oflarge image databases

To realize content-hased retrieval of large image databases, it is required to develop an efficient index and retrieval scheme. This paper proposes an index algorithm of clustering called CMA, which supports fast retrieval of large image databases. CMA takes advantages of k-means and self-adaptive algorithms. It is simple and works without any user interactions. There are two main stages in this algorithm. In the first stage, it classifies images in a database into several clusters, and automatically gets the necessary parameters for the next stage-k-means iteration. The CMA algorithm is tested on a large database of more than ten thousand images and compare it with k-means algorithm. Experimental results show that this algorithm is effective in both precision and retrieval time.

Establishment of Costume Design Database Based on Image Resources in Daur Nationality

Through collecting, summarizing and effectively classifying the image resources in Daur nationality,and applying the theories and methods of costume design,ethnology and computer science,it investigates into the retrieval method and indicator system of images resources in Daur nationality in the context of digitalization. The preliminarily established application model of Daur costume design database can search out all qualified image resources from the database quickly according to the prerequisites given by users so that they can obtain information promptly. In particular,costume designers can get inspirations of creation and enlightenment on their creative thinking. Therefore,the effective integration of art and scientific researches can be realized.

Design and Realization of Image Database of Pitaya Disease

With the expansion of cultivation scale,pitaya diseases are gradually increasing.Traditionally relying on human observation to judge the disease is limited by the skills and experience of the observer,which cannot guarantee the accuracy and real-time of the judgment,and consumes much manpower and time.In this study,by collecting,segmenting,and labeling images of 4 main diseases of pitaya in the field,an image database of main diseases of pitaya in the field was constructed to provide a basis for computer image recognition of pitaya diseases.Thereby,it benefits reducing manual error and improving the accuracy and real-time of disease identification for agricultural production,but also lays a foundation for the future development of intelligent agriculture.

A solution for digital image management in spatial databases

Digital Orthographic Map (DOM) can be used in various applications because it contains both image features and terrain information. Spatial database management systems aim at the effective and efficient management of data related to a space, engineering design and so on. Thereby spatial database provides an efficient solution for managing DOM. According to large amounts of the DOM data in storage, a data compression based on wavelet is introduced into the storage. Another strategy to solve this problem is to decompose the raw image into tiles and store the tiles individually as separate tuples. The metadata of DOM can be used to organize and manage spatial information, especially for spatial data sharing and fast locating. A tool for browsing, zooming and querying the DOM data is also designed. We implemented these ideas in SISP(Spatial Information Sharing System) and applied the subsystem into the DOM management of Beijing City, which is an component of the Beijing Spatial Information Infrastructure.

CASIA-Iris-Africa:A Large-scale African Iris Image Database

Iris biometrics is a phenotypic biometric trait that has proven to be agnostic to human natural physiological changes.Research on iris biometrics has progressed tremendously,partly due to publicly available iris databases.Various databases have been available to researchers that address pressing iris biometric challenges such as constraint,mobile,multispectral,synthetics,long-distance,contact lenses,liveness detection,etc.However,these databases mostly contain subjects of Caucasian and Asian docents with very few Africans.Despite many investigative studies on racial bias in face biometrics,very few studies on iris biometrics have been published,mainly due to the lack of racially diverse large-scale databases containing sufficient iris samples of Africans in the public domain.Furthermore,most of these databases contain a relatively small number of subjects and labelled images.This paper proposes a large-scale African database named Chinese Academy of Sciences Institute of Automation(CASIA)-Iris-Africa that can be used as a complementary database for the iris recognition community to mediate the effect of racial biases on Africans.The database contains 28717 images of 1023 African subjects(2046 iris classes)with age,gender,and ethnicity attributes that can be useful in demographically sensitive studies of Africans.Sets of specific application protocols are incorporated with the database to ensure the database’s variability and scalability.Performance results of some open-source state-of-the-art(SOTA)algorithms on the database are presented,which will serve as baseline performances.The relatively poor performances of the baseline algorithms on the proposed database despite better performance on other databases prove that racial biases exist in these iris recognition algorithms.

访问MS SQL Server Image类型数据的一种简便方法

介绍了一种用ＶＢ或Ｄｅｌｐｈｉ作为前端程序开发工具时访问大型数据库ＭＳ ＳＱＬ Ｓｅｒｖｅｒ数据表Ｉｍａｇｅ列的简便方法。

用ASP.NET实现对Image数据的存取

介绍了ADO.NET技术的特点,结合对图片和Office文档上载下载的实例,说明如何在ASP.NET中通过ADO.NET访问网络数据库,讨论ASP.NET对数据库中的image类型数据进行存取的实现方法,并给出部分程序代码。

Visual Basic.NET环境下存取数据库的Image字段数据

本文主要讲解在Visual Basic．NET中对数据库中特殊数据处理的一般方法，以帮助大家了解Imag字段的存取步骤和常用控件。

在PowerBuilder中读写Sybase的Image数据

Ｉｍａｇｅ类型数据的读写是数据库系统中的一个难点，本文探讨了在广为流行的数据库开发工具ＰｏｗｅｒＢｕｉｌｄｅｒ环境下，读写Ｓｙｂａｓｅ数据库中ｉｍａｇｅ类型数据的方法。

基于VB.NET的Image类型数据的存取

与其它的数据类型不同,Image类型的数据不能直接存储和读取。通过My.Computer.FileSystem对象可以方便地将图像文件与数组进行转换,从而提供一个快捷的访问数据库Image类型数据的方法。

用ASP.NET实现对Image类型数据的存取

通过对ADO.NET技术特点的简要介绍,提出了对数据库中Image类型的数据进行存取的实现方法。结合具体实例,说明在Web数据库应用系统中,如何运用ASP.NET技术实现SQLSERVER2000数据库对图片文件进行存储并通过浏览器进行显示。该实例为数据库中多种二进制大对象类型的数据,包括图片、声音、视频及各种Office文档等进行存储与显示提供了一种可行的有效方法,并且给出部分程序源代码。

Color-based Image Retrieval Using Sub-range Cumulative Histogram

A new techinque for color based image retrieval is proposed. In this technique, the whole spectrum of a color image is divided into several sub ranges according to human visual characteristics. Then for each sub range, the cumulative histogram is used for similarity matching. It is shown that the color contents of image can be well captured by the sub range cumulative histogram. The new technique has been tested and compared with conventional techniques with the help of a database of 400 images of real flowers, which are quite complicated in color contents. Some satisfactory retrieval results are presented.

AUTOMATIC SEARCHING OF CONTROL POINT IN NOAA AVHRR IMAGE

This paper discusses the approaches for automatical searching of control points in the NOAA AVHRR image on the basis of data rearrangement in the form of latitude and longitude grid. The vegetation index transformation and multi-level matching strategies have been proven effective and successful as the experiments show while the control point database is established.