Spatial Pattern Spectra and Content-based Image Retrieval

Granulometries are powerful and versatile tools in image analysis and pattern spectra, or size distribulions, are a simple method of extracting information from an image using these granulometries. One of the drawbacks of the traditional pattern spectra, the lack of spatial information about connected components within images, is addressed in this project by introducing three extensions to the regular area pattern spectra: one based on moments, one based on translation of the components within the image, and one based on multi-scale connectivity. These three extensions are tested in the field of content-based image retrieval: are they able to retrieved images from an image-database, that are similar in some way to a certain, user-provided, query-image? This is a question that is interesting for fields like intelligent multimedia and web searches (search engines).

Color constancy using natural image statistics and scene semantics

Existing color constancy methods are all based on specific assumptions such as the spatial and spectral characteristics of images. As a consequence, no algorithm can be considered as universal. However, with the large variety of available methods, the question is how to select the method that performs best for a specific image. To achieve selection and combining of color constancy algorithms, in this paper natural image statistics are used to identify the most important characteristics of color images. Then, based on these image characteristics, the proper color constancy algorithm (or best combination of algorithms) is selected for a specific image. To capture the image characteristics, the Weibull parameterization (e.g., grain size and contrast) is used. It is shown that the Weibull parameterization is related to the image attributes to which the used color constancy methods are sensitive. An MoG-classifier is used to learn the correlation and weighting between the Weibull-parameters and the image attributes (number of edges, amount of texture, and SNR). The output of the classifier is the selection of the best performing color constancy method for a certain image. Experimental results show a large improvement over state-of-the-art single algorithms. On a data set consisting of more than 11,000 images, an increase in color constancy performance up to 20 percent (median angular error) can be obtained compared to the best-performing single algorithm. Further, it is shown that for certain scene categories, one specific color constancy algorithm can be used instead of the classifier considering several algorithms