Peter M. F. Kisters

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A unique color space segmentation method is introduced. It is founded on features of human cognition, where 11 color categories are used in processing color. In two experiments, human subjects were asked to categorize color stimuli into these 11 color categories, which resulted in markers for a Color LookUp Table (CLUT). These CLUT markers are projected on(More)
From a human centered perspective three ingredients for Content-Based Image Retrieval (CBIR) were developed. First, with their existence confirmed by experimental data, 11 color categories were utilized for CBIR and used as input for a new color space segmentation technique. The complete HSI color space was divided into 11 segments (or bins), resulting in a(More)
In Content-Based Image Retrieval (CBIR) two query-methods exist: query-by-example and query-by-memory. The user either selects an example image or selects image features retrieved from memory (such as color, texture, spatial attributes, and shape) to define his query. Hitherto, research on CBIR interfaces was absent. Hence, a usability evaluation of(More)
We present the concept of intelligent Content-Based Image Retrieval (iCBIR), which incorporates knowledge concerning human cognition in system development. The present research focuses on the utilization of color categories (or focal colors) for CBIR purposes, particularly considered to be useful for query-by-heart purposes. However, this research explores(More)
Digital media are rapidly replacing their analog counterparts. This development is accompanied by (i) the increasing amount of images present on the Internet, (ii) the availability of the Internet for an increasing number of people, (iii) a decline in digital storage costs, and (iv) the developments in personal digital video/photo cam-era's [1, 2, 3]. In(More)
The following full text is a preprint version which may differ from the publisher's version. Abstract. A new method is introduced for describing, visualizing, and inspecting data spaces. It is based on an adapted version of the Fast Exact Euclidean Distance (FEED) transform. It computes a description of the complete data space based on partial data.(More)
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