A Trans-Disciplinary Vocabulary for Assessing the Visual Performance of BIPV

It is widely acknowledged that the visual dimension of photovoltaics (PV) is fundamental for social acceptance. In this sense, the so-called Building Integrated Photovoltaics (BIPV) is a possible catalyzer, as PV is hidden (integrated) into building envelope morphologies that are familiar to the public. It is crucial to be able to design and assess a BIPV system so that its visual performance is optimal. Many studies exist in this regard, but still they do not deliver a clear theoretical organization of the concepts used for defining the visual performance of BIPV. This paper elaborates a trans-disciplinary systemic formalization of BIPV and proposes a vocabulary focusing on the formal perception of BIPV as a part of the building’s envelope system. The proposed vocabulary is based on a set of 11 visual keywords; as the proposed method unifies the formal and the cognitive information contents. It will facilitate the dialogue among different stakeholders (e.g., architects, clients, modules manufacturers, and public authorities) and, in general, the visual performance assessment of BIPV. In consequence, it allows for objective comparison and thus informed decision-making.

Abstractions in Intelligent Multimedia Databases

Semantic characterization is necessary for developing intelligent multimedia databases, because humans tend to search for media content based on their inherent semantics. However, automated inference of semantic concepts derived from media components stored in a database is still a challenge. The aim of this chapter is to demonstrate how layered architectures and “visual keywords” can be used to develop intelligent search systems for multimedia databases. The layered architecture is used to extract meta-data from multimedia components at various layers of abstractions. While the lower layers handle physical file attributes and low-level features, the upper layers handle high-level features and attempts to remove ambiguities inherent in them. To access the various abstracted features, a query schema is presented, which provides a single point of access while establishing hierarchical pathways between feature-classes. Minimization of the semantic gap is addressed using the concept of “visual keyword” (VK). “Visual keywords” are segmented portions of images with associated low- and high-level features, implemented within a semantic layer on top of the standard low-level features layer, for characterizing semantic content in media components. Semantic information is however predominantly expressed in textual form, and hence is susceptible to the limitations of textual descriptors – viz. ambiguities related to synonyms, homonyms, hypernyms, and hyponyms. To handle such ambiguities, this chapter proposes a domain specific ontology-based layer on top of the semantic layer, to increase the effectiveness of the search process.

A Novel Framework for Image Categorization and Automatic Annotation

Image classification and automatic annotation could be treated as effective solutions to enable keyword-based semantic image retrieval. Traditionally, they are investigated in different models separately. In this chapter, we propose a novel framework uniting image classification and automatic annotation by learning semantic concepts of image categories. To choose representative features, feature selection strategy is proposed and visual keywords are constructed, including discrete method and continuous method. Based on the selected features, the Integrated Patch (IP) model is proposed to describe the image category. As a generative model, the IP model describes the appearance of the combination of the visual keywords, considering the diversity of the object. The parameters are estimated by EM algorithm. The experimental results on Corel image dataset and Getty Image Archive demonstrate that the proposed feature selection and image description model are effective in image categorization and automatic image annotation, respectively.