Multi-Index Image Retrieval Hash Algorithm Based on Multi-View Feature Coding

In recent years,with the massive growth of image data,how to match the image required by users quickly and efficiently becomes a challenge.Compared with single-view feature,multi-view feature is more accurate to describe image information.The advantages of hash method in reducing data storage and improving efficiency also make us study how to effectively apply to large-scale image retrieval.In this paper,a hash algorithm of multi-index image retrieval based on multi-view feature coding is proposed.By learning the data correlation between different views,this algorithm uses multi-view data with deeper level image semantics to achieve better retrieval results.This algorithm uses a quantitative hash method to generate binary sequences,and uses the hash code generated by the association features to construct database inverted index files,so as to reduce the memory burden and promote the efficient matching.In order to reduce the matching error of hash code and ensure the retrieval accuracy,this algorithm uses inverted multi-index structure instead of single-index structure.Compared with other advanced image retrieval method,this method has better retrieval performance.

A Mechanism for Dynamic and Consistent Product Information Association

The dynamic and consistent information association among vtrious application activities in the full life cycle of a product is a key to the assurance of the cooperation among different application domains. In order to establish and maintain the association, a design-process-based product association model was proposed. This model takes advantage of the generic naming mechanism, the private protocol for history-based form feature modeling, on which the Data Association Protocol is built. Hence the model can provide the way of constructing and maintaining the information linkage among different product developing stages naturally and dynamically while keeping the privacy of the feature coding. A case study illustrates the utilities of the model in the data linking between design model and process planning model.

B2SMatcher:fine-Grained version identification of open-Source software in binary files

Codes of Open Source Software(OSS)are widely reused during software development nowadays.However,reusing some specific versions of OSS introduces 1-day vulnerabilities of which details are publicly available,which may be exploited and lead to serious security issues.Existing state-of-the-art OSS reuse detection work can not identify the specific versions of reused OSS well.The features they selected are not distinguishable enough for version detection and the matching scores are only based on similarity.This paper presents B2SMatcher,a fine-grained version identification tool for OSS in commercial off-the-shelf(COTS)software.We first discuss five kinds of version-sensitive code features that are trackable in both binary and source code.We categorize these features into program-level features and function-level features and propose a two-stage version identification approach based on the two levels of code features.B2SMatcher also identifies different types of OSS version reuse based on matching scores and matched feature instances.In order to extract source code features as accurately as possible,B2SMatcher innovatively uses machine learning methods to obtain the source files involved in the compilation and uses function abstraction and normalization methods to eliminate the comparison costs on redundant functions across versions.We have evaluated B2SMatcher using 6351 candidate OSS versions and 585 binaries.The result shows that B2SMatcher achieves a high precision up to 89.2%and outperforms state-of-the-art tools.Finally,we show how B2SMatcher can be used to evaluate real-world software and find some security risks in practice.

Color space quantization-based clustering for image retrieval

Color descriptors of an image are the most widely used visual features in content-based image retrieval sys- tems. In this study, we present a novel color-based image retrieval framework by integrating color space quantization and feature coding. Although color features have advantages such as robustness and simple extraction, direct processing of the abundant amount of color information in an RGB image is a challenging task. To overcome this problem, a color space clustering quantization algorithm is proposed to obtain the clustering color space （CCS） by clustering the CIE1976L＊a＊b＊ space into 256 distinct colors, which ade- quately accommodate human visual perception. In addition, a new feature coding method called feature-to-character coding （FCC） is proposed to encode the block-based main color fea- tures into character codes. In this method, images are repre- sented by character codes that contribute to efficiently build- ing an inverted index by using color features and by utilizing text-based search engines. Benefiting from its high-efficiency computation, the proposed framework can also be applied to large-scale web image retrieval. The experimental results demonstrate that the proposed system can produce a signifi- cant augmentation in performance when compared to block- based main color image retrieval systems that utilize the tra- ditional HSV（Hue, Saturation, Value） quantization method.