Exploiting multimedia in creating and analysing multimedia Web archives

The data contained on the web and the social web are inherently multimedia and consist of a mixture of textual, visual and audio modalities. Community memories embodied on the web and social web contain a rich mixture of data from these modalities. In many ways, the web is the greatest resource ever created by human-kind. However, due to the dynamic and distributed nature of the web, its content changes, appears and disappears on a daily basis. Web archiving provides a way of capturing snapshots of (parts of) the web for preservation and future analysis. This paper provides an overview of techniques we have developed within the context of the EU funded ARCOMEM (ARchiving COmmunity MEMories) project to allow multimedia web content to be leveraged during the archival process and for post-archival analysis. Through a set of use cases, we explore several practical applications of multimedia analytics within the realm of web archiving, web archive analysis and multimedia data on the web in general

A novel method for extracting and recognizing logos

Nowadays, the high volume of archival documents has made it exigent to store documents in electronic databases. A text logo represents the ownership of the text, and different texts can be categorized by it; for this reason, different methods have been presented for extracting and recognizing logos. The methods presented earlier, suffer problems such as, error of logo detection and recognition and slow speed. The proposed method of this study is composed of three sections: In the first section, the exact position of the logo can be identified by the pyramidal tree structure and horizontal and vertical analysis, and in the second section, the logo can be extracted through the algorithm of the boundary extension of feature rectangles. In the third section, after normalizing the size of the logo and eliminating the skew angle, for feature extraction, we first blocked the region encompassing the logo, and then we extract a particular feature by the parameter of the center of gravity of connected component each block. Finally, we use the KNN classification for the recognition of the logo.DOI:http://dx.doi.org/10.11591/ijece.v2i5.129

Stamp detection in scanned documents

The article presents current challenges in stamp detection problem. It is a crucial topic these days since more and more traditional paper documents are being scanned in order to be archived, sent through the net or just printed. Moreover, an electronic version of paper document stored on a hard drive can be taken as forensic evidence of possible crime. The main purpose of the method presented in the paper is to detect, localize and segment stamps (imprints) from the scanned document. The problem is not trivial since there is no such thing like stamp standard. There are many variations in size, shape, complexity and ink color. It should be remembered that the scanned document may be degraded in quality and the stamp can be placed on a relatively complicated background. The algorithm consists of several steps: color segmentation and pixel classification, regular shapes detection, candidates segmentation and verification. The paper includes also the initial results of selected experiments on real documents having different types of stamps

Deep Learning for Logo Detection: A Survey

When logos are increasingly created, logo detection has gradually become a research hotspot across many domains and tasks. Recent advances in this area are dominated by deep learning-based solutions, where many datasets, learning strategies, network architectures, etc. have been employed. This paper reviews the advance in applying deep learning techniques to logo detection. Firstly, we discuss a comprehensive account of public datasets designed to facilitate performance evaluation of logo detection algorithms, which tend to be more diverse, more challenging, and more reflective of real life. Next, we perform an in-depth analysis of the existing logo detection strategies and the strengths and weaknesses of each learning strategy. Subsequently, we summarize the applications of logo detection in various fields, from intelligent transportation and brand monitoring to copyright and trademark compliance. Finally, we analyze the potential challenges and present the future directions for the development of logo detection to complete this survey