| dc.description.abstract | The colors embedded in an image are firstly analyzed. Then, the results are applied to color quantization, color image retrieval and face detection. In the dissertation, an adaptive clustering algorithm for color image quantization is presented first. In our approach, a superposed 3D histogram is first calculated. Then, the sorted histogram list is fed into an adaptive clustering algorithm to extract the palette colors in the image. Finally, a destined pixel mapping algorithm is applied to classify pixels into their corresponding palette colors. The quantized error of our proposed algorithm is very small due to the combination of the reduced RGB color space utilization and the adaptive clustering algorithm. Besides, the executing speed of our proposed algorithm is also quite fast due to the reduced RGB color space, sorted histogram list, suitable color design and destined pixel mapping. Experimental results reveal the feasibility and superiority of our proposed approach in solving color quantization problem.
Secondly, a novel region-based multiple classifier color image retrieval system is presented. In our approach, a region-growing technique is firstly employed to cluster connected color pixels with the same color in an image to form color regions which are the primitive elements utilized in our proposed approach. Then, three complementary region-based classifiers are selected in the classifier selection stage, which include color classifier, shape classifier and relational classifier. In each classifier, a virtual probability representing the probability that an image is similar to the query image is defined. Thereafter, a set of virtual probabilities is calculated in each classifier. Next, the measurement dependent methods are applied to combine the virtual probabilities of classifiers in the decision combination stage. Besides, the dynamic selection scheme designed in the decision combination stage can further improve the system performance dramatically. Experimental results further reveal the feasibility and validity of our proposed approach in solving color image retrieval problem.
Lastly, a novel face detection algorithm is presented to locate multiple faces in color scenery images. A binary skin color map is first obtained in the color analysis stage. Then, color regions corresponding to the facial and non-facial areas in the color map are separated with a clustering-based splitting algorithm. Thereafter, an elliptic face model is devised to crop the real human faces through the shape location procedure. Last, local thresholding technique and a statistic-based verification procedure are utilized to confirm the human faces. The proposed detection algorithm combines both the color and shape properties of faces. In this work, the color span of human face can be expanded as wilder as possible to cover different faces by using the clustering-based splitting algorithm. Experimental results also reveal the feasibility of our proposed approach in solving face detection problem. | en_US |