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Content-Based Image Retrieval Using Computational Intelligence Techniques
Published in Kim-Hui Yap, Ling Guan, Stuart William Perry, Hau-San Wung, Adaptive Image Processing, 2018
Kim-Hui Yap, Ling Guan, Stuart William Perry, Hau-San Wung
Various CBIR systems have been proposed and developed in recent years, including QBIC [246], MARS [198], Virage [223], Photobook [247], VisualSEEk [248], PicToSeek [249], and PicHunter [250]. Among the CBIR schemes, relevance feedback is a popular technique that has been introduced to bridge the semantic gap between the high-level human perception and low-level visual features [198,249–266]. Relevance feedback is an interactive mechanism where the users provide feedback on the relevance of the retrieved images, and the system learns the user information needs based on this feedback. Many relevance feedback algorithms have been developed in CBIR and demonstrated considerable performance improvement [198,249–266]. These include query refinement [198], feature reweighting [251,252], statistical learning [250,254,255], neural networks [256–260], and support vector machine (SVM) [261–265].
The Participation of Users in Systems Design: An Account of the Origin, Evolution, and Use of the ETHICS Method
Published in Douglas Schuler, Aki Namioka, Participatory Design, 2017
This analysis of information needs is done twice more. Once to identify information that is highly desirable but not essential; and once to do the same for information that is useful but could be done without. The specification of essential information needs will provide a guide to what a computer-based information system must provide if it is to be successful. Information that is highly desirable and useful may be provided by a computer but can also be improved through better verbal and written communication. Clear objectives for the new system should now be agreed and discussed with the project steering group and the constituents of the design group members.
Application of the Fact-Based Approach to Domain Modeling of Object-Oriented Information Systems
Published in Roger H.L. Chiang, Keng Siau, Bill C. Hardgrave, Systems Analysis and Design, 2017
Among the tasks of object-oriented modeling, the construction of a correct domain model— one that captures all the relevant domain classes and their relationships—is critical. A successful information system must, by necessity, provide the correct functionality in order to satisfy the business’s information needs for which the system is built. The domain model is in fact a model of those information needs, and as such, it plays a crucial role in the development process.
Information processing in the “not-in-my-backyard” strategy: An empirical study of anti-nuclear behavioral responses
Published in Human and Ecological Risk Assessment: An International Journal, 2020
Xiaoli Hu, Yundong Xie, Shaofeng Zhang
In recent years, scholars have continually perfected the HSM. By integrating the theory of planned behavior into the original HSM, Griffin et al. (1999) proposed the risk information-seeking model (RISM), which further explained the phenomenon of purposeful seeking for specific information to make correct behavior decisions. The RISM proposes that information need is a direct predictor for a person seeking out information (Zeng et al. 2017). Moreover, Zhu et al. (2016) assumes information seeking is an antecedent to information processing. Consequently, information need is the key element of the model, which is the starting point and the internal driving force for information seeking and information processing. Specifically, information need arises from a gap between the sufficiency threshold of information required for certain goals and the amount of currently held information (Griffin et al. 1999). When the information individuals need to know is less than the knowledge individuals have, information needs are generated (Huurne and Gutteling 2008). The purposive seeking for information is the core strategy to satisfy information needs (Wilson 2000). Information seeking is an achievable process, because information technology offers the public credible and effective information sources to seek relevant risk information, such as traditional media, new media, social media, and interpersonal communication (Lu et al. 2019). Indeed, the willingness to obtain information and the behavior of obtaining information are the practical conditions for information processing (Kahlor et al. 2018).