My Computer Speaks Colors! Fuzzy Color Spaces for Image Understanding, Description and Retrieval
Jesus Chamorro Martinez
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09 Oct 2019
Jesus Chamorro Martinez, University of Granada, Granada, Spain
Abstract:The development of ICT technologies, especially those related to mobile devices, has increased the use of digital images in everyday life. As a consequence, there is a growing demand for solutions allowing us an intelligent and friendly interaction with visual data. This interaction requires technical proposals to cover the gap between the unstructured data represented by the images, and the semantics of the natural language (known as the semantic gap).
As a particular case, color is one of the most important visual properties used to describe the objects in a given scene and, consequently, to interact with the system (for example, to refer to a certain object � �look at the red car� � or to make a query � �I�m looking for images where green predominates�-). But, how can we generate automatically a correct expression of the color perceived in an image using linguistic color terms? The complexity of the problem increases if we take into account that color categories have ill-defined boundaries, and they are subjective and context dependent.
To face the above question, in this talk the notions of fuzzy color and fuzzy color space are introduced for modelling the correspondence between computational representation of colors and perceptual color categories identified by a color name. A methodology for learning fuzzy colors based on G�rdenfors� paradigm of Conceptual Spaces is presented. First, we analyze approaches focused on modelling collections of independent color categories, which define fuzzy boundaries of color categories on the basis of a tessellation of a given color space. Second, new proposals based on IS-A relationships between color categories are introduced, allowing us to define color terms (for example, �red�) by means of the aggregation of related subcategories (�vivid red�, �pale red�, �dark red�, etc.); this approach provides models which are more coherent with human intuition, since it does not consider color categories as mutually exclusive, and allows us to work at different levels of detail when analyzing or processing an image. In addition, the notion of fuzzy color space is formalized as the collection of fuzzy colors corresponding to the color categories employed in a certain context/application and/or for a specific user.
The above approaches are illustrated by defining fuzzy color spaces, using an open source software we have developed, on the basis of the well-known ISCC-NBS color naming system, as well as others based on collections of color names and prototypes provided by users. In addition, the suitability of fuzzy color spaces is shown for different applications including image description and retrieval.
Abstract:The development of ICT technologies, especially those related to mobile devices, has increased the use of digital images in everyday life. As a consequence, there is a growing demand for solutions allowing us an intelligent and friendly interaction with visual data. This interaction requires technical proposals to cover the gap between the unstructured data represented by the images, and the semantics of the natural language (known as the semantic gap).
As a particular case, color is one of the most important visual properties used to describe the objects in a given scene and, consequently, to interact with the system (for example, to refer to a certain object � �look at the red car� � or to make a query � �I�m looking for images where green predominates�-). But, how can we generate automatically a correct expression of the color perceived in an image using linguistic color terms? The complexity of the problem increases if we take into account that color categories have ill-defined boundaries, and they are subjective and context dependent.
To face the above question, in this talk the notions of fuzzy color and fuzzy color space are introduced for modelling the correspondence between computational representation of colors and perceptual color categories identified by a color name. A methodology for learning fuzzy colors based on G�rdenfors� paradigm of Conceptual Spaces is presented. First, we analyze approaches focused on modelling collections of independent color categories, which define fuzzy boundaries of color categories on the basis of a tessellation of a given color space. Second, new proposals based on IS-A relationships between color categories are introduced, allowing us to define color terms (for example, �red�) by means of the aggregation of related subcategories (�vivid red�, �pale red�, �dark red�, etc.); this approach provides models which are more coherent with human intuition, since it does not consider color categories as mutually exclusive, and allows us to work at different levels of detail when analyzing or processing an image. In addition, the notion of fuzzy color space is formalized as the collection of fuzzy colors corresponding to the color categories employed in a certain context/application and/or for a specific user.
The above approaches are illustrated by defining fuzzy color spaces, using an open source software we have developed, on the basis of the well-known ISCC-NBS color naming system, as well as others based on collections of color names and prototypes provided by users. In addition, the suitability of fuzzy color spaces is shown for different applications including image description and retrieval.
