| dc.description.abstract | Histogram matching is a commonly adopted technique in the applications of pattern recognition. The matching of two patterns can thus be accomplished by matching their corresponding histograms. In this dissertation, we comprehensively analyze the characteristics of histogram and discuss the issues frequently appeared in image retrieval by utilizing the detail analyzing results.
The characteristics inherent in histograms that we study are histogram capacity and histogram smoothing. Through the analysis of histogram capacities, researchers can evaluate and compare the discrimination capabilities of different histogram measurements or histograms embedded with different features. As to the histogram smoothing technique, it can compensate the penalty resulting from the enlargement of histogram dissimilarities of similar patterns due to the increase of discrimination capability.
Moreover, an automatic similarity judgment mechanism without human involving for the evaluation of the retrieval effectiveness of image retrieval algorithms is proposed. Traditionally, researchers usually use the statistic scores of similarity judgments deriving from a few persons to demonstrate the performance of their image retrieval systems. However, the statistic scores of similarity judgment are too subjective. Through the use of our proposed similarity judgment mechanism, the retrieval effectiveness of image retrieval systems can be evaluated objectively, transparently and quantitatively.
As we know, the number of features and the resolution of each feature will determine the size of histograms. Usually, the more the number of features and the higher the resolution of each feature, the stronger the discrimination capability of histograms will be. Unfortunately, it will lead to the decrease of the efficiency of histogram matching because traditional algorithms in evaluating similarity are all relevant to the histogram size. In this dissertation, a novel histogram-matching algorithm is proposed whose efficiency is irrelevant to the histogram size. By adopting our proposed algorithm, future researchers can focus more on the selection and combination of histogram features and freely adjust the resolution of each feature without worrying the decrease of retrieval efficiency.
To demonstrate the feasibility and validity of the analyzing results and the proposed algorithms, a similar image retrieval experiment, which was constructed based on multi-feature histogram, is conducted. Although the experiment itself is the image retrieval application, the proposed histogram-relating techniques are not merely limited to be employed to image patterns. They can be applied to other retrieval-like applications which are independent of images or even other fields irrelevant to retrieval-like applications. | en_US |