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Developing the Management Systems of the 1990s: The Role of Collaborative Work
Published in Margrethe H. Olson, Technological Support for Work Group Collaboration, 2020
Knowledge-intensive collaboration systems inhabit the low formality/low flexibility quadrant, and are so called because they generally support interactions in which exchange of knowledge or information is done in ways not governed by strict organizational procedures. Systems in this quadrant include electronic mail systems,7 computer and video conferencing systems, real-time shared-screen conferencing systems (Sarin & Greif, 1985), meeting augmentation systems8 such as Colab (Stefik et al., 1987) and Project NICK (Begeman et al., 1986), and hypertext/cooperative authoring systems such as Augment (Engelbart, 1984a, 1984b; Engelbart, Watson, & Norton, 1973), Intermedia (Garrett, Smith, & Meyrowitz, 1986), and NoteCards (Trigg, Suchman, & Halasz, 1986).
Distributed image retrieval with colour and keypoint features
Published in Journal of Information and Telecommunication, 2019
Michał Ła̧giewka, Marcin Korytkowski, Rafal Scherer
Content-based image retrieval (CBIR) has become recently well established in the literature. Yet, nearly all of the solutions presented so far are not designed nor suited for relational databases. Relational databases reign supreme in the business world but storing a huge amount of undefined and unstructured binary data and its fast and efficient search and retrieval is a problem for them. Examples of such data are images or video files. One, old solution devoted to storage and retrieval of images in a database is the methodology proposed in Ogle and Stonebraker (1995), where PostgreSQL database server was used to store and compare images by colour-based features. There were also attempts to implement CBIR in commercial database systems. An example might be the Oracle database environment called ‘interMedia’, where image retrieval is based on the global colour representation, low-level patterns and textures within the image, such as graininess or smoothness, the shapes that appear in the image created by a region of uniform colour and their location. It was described in Oracle Database Online Documentation (10 g Release 2, Chapter 6, Content-Based Retrieval Concepts) but was abandoned in newer Oracle versions. Thus, the standard SQL language does not have commands for handling multimedia data and image files are stored often directly in database tables which causes low efficiency of the whole system and even time-consuming data backup. To address these problems, the authors proposed earlier (Korytkowski, 2017; Korytkowski, Rutkowski, & Scherer, 2016) a CBIR system that was able to store and index images in a relational database with local interest points. Local invariant features have gained a wide popularity (Scherer, 2019) with the most popular local keypoint detectors and descriptors SURF (Bay, Ess, Tuytelaars, & Van Gool, 2008), SIFT (Lowe, 2004) or ORB (Rublee, Rabaud, Konolige, & Bradski, 2011). In the previous work and this paper, we use 128-element SIFT descriptors. The system also allowed to query the database about the image content by SQL commands. Information about local visual features was indexed in the relational database by fuzzy sets (Cpalka, Lapa, Przybyl, & Zalasinski, 2014; Harmati, Bukovics, & Koczy, 2016; Łapa, Szczypta, & Venkatesan, 2012; Prasad et al., 2017; Scherer, Smolag, & Gaweda, 2016; Stanovov, Semenkin, & Semenkina, 2016). and the AdaBoost algorithm (Viola & Jones, 2001). The mechanism used for database image indexing is depicted in details in Korytkowski et al. (2016) and Korytkowski (2017). Usually, classifiers are used for the purposes they are intended (Hoang, 2017; Pham, Nguyen, Tran, Nguyen, & Ha, 2017), but in the paper, we use weak classifiers to obtain distinctive features for a given visual class. Currently, deep learning-based approaches (Bologna & Hayashi, 2017; Chang, Constante, Gordon, & Singana, 2017) are gaining popularity in image analysis, but they are slower than the proposed approach and not suitable for relational database purposes.