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Eleven colour-emotion scales, warm–cool, heavy–light, modern–classical, clean–dirty, active–passive, hard–soft, harmonious–disharmonious, tense–relaxed, fresh–stale, masculine–feminine, and like–dislike, were investigated on 190 colour pairs with British and Chinese observers. Experimental results show that gender difference existed in masculine–feminine, whereas no significant cultural difference was found between British and Chinese observers. Three colour-emotion factors were identified by the method of factor analysis and were labeled “colour activity,” “colour weight,” and “colour heat.” These factors were found similar to those extracted from the single colour emotions developed in Part I. This indicates a coherent framework of colour emotion factors for single colours and two-colour combinations. An additivity relationship was found between single-colour and colour-combination emotions. This relationship predicts colour emotions for a colour pair by averaging the colour emotions of individual colours that generate the pair. However, it cannot be applied to colour preference prediction. By combining the additivity relationship with a single-colour emotion model, such as those developed in Part I, a colour-appearance-based model was established for colour-combination emotions. With this model one can predict colour emotions for a colour pair if colour-appearance attributes of the component colours in that pair are known. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 292–298, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20024

In this study three colour preference models for single colours were developed. The first model was developed on the basis of the colour emotions, clean–dirty, tense–relaxed, and heavy–light. In this model colour preference was found affected most by the emotional feeling “clean.” The second model was developed on the basis of the three colour-emotion factors identified in Part I, colour activity, colour weight, and colour heat. By combining this model with the colour-science-based formulae of these three factors, which have been developed in Part I, one can predict colour preference of a test colour from its colour-appearance attributes. The third colour preference model was directly developed from colour-appearance attributes. In this model colour preference is determined by the colour difference between a test colour and the reference colour (L*, a*, b*) = (50, −8, 30). The above approaches to modeling single-colour preference were also adopted in modeling colour preference for colour combinations. The results show that it was difficult to predict colour-combination preference by colour emotions only. This study also clarifies the relationship between colour preference and colour harmony. The results show that although colour preference is strongly correlated with colour harmony, there are still colours of which the two scales disagree with each other. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 381–389, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20047

Many stages in the product design and development process, particularly the latter stages, are well supported by computers. Computer Supported Co-operative Design (CSCD), however, embraces the use of computers to support a greater number of various tasks such as relationships (e.g. designer to designer communications), processes (e.g. CAD/CAM) and specific design tasks (e.g. concept generation). CSCD has been developed as a means of helping organizations produce higher quality and more diverse products in the light of several significant pressures. It should be noted, however, that no one system can support the diversity of design tasks which form the fundamental basis of creative design activity.

Querying of visual databases has relied predominantly on text based systems. Words do not provide an appropriate or adequate means of describing visual artifacts. A system (DIVIUS) has been developed which allows users to describe and query objects in a pictorial database, using a visual language derived from the database objects. Users can also indicate their level of uncertainty regarding certain attributes of the query.