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Image retrieval by color semantics
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AI
DOI：10.1007/s005300050120.png)
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摘要
En 中文
The development of a system supporting querying of image databases by color content tackles a major design choice about properties of colors which are referenced within user queries. On the one hand, low-level properties directly reflect numerical features and concepts tied to the machine representation of color information. On the other hand, high-level properties address concepts such as the perceptual quality of colors and the sensations that they convey. Color-induced sensations include warmth, accordance or contrast, harmony, excitement, depression, anguish, etc. In other words, they refer to the semantics of color usage. In particular, paintings are an example where the message is contained more in the high-level color qualities and spatial arrangements than in the physical properties of colors. Starting from this observation, Johannes Itten introduced a formalism to analyze the use of color in art and the effects that this induces on the user's psyche. In this paper, we present a system which translates the Itten theory into a formal language that expresses the semantics associated with the combination of chromatic properties of color images. The system exploits a competitive learning technique to segment images into regions with homogeneous colors. Fuzzy sets are used to represent low-level region properties such as hue, saturation, luminance, warmth, size and position. A formal language and a set of model-checking rules are implemented to define semantic clauses and verify the degree of truth by which they hold over an image.
Keyword:
image retrieval by content
color semantics
image databases
