近幾年來，因為網際網路(Internet)的蓬勃發展、資訊技術的進步和多媒體設備日趨普遍，使得數位資料的數量與日遽增，其中影像(image)是多媒體資料中較常被應用的一種。為了能有效的分析與管理影像資料，首先就是要如何透過電腦以有效的方法去建檔和分析這些龐大的影像資料量。這要透過使用適當的特徵(feature)來描述影像，再經由這些描述讓使用者可以對影像資料進行檢索和瀏覽等等。
有效的影像檢索技術必須充分利用到影像的語意訊息。因此，如何有效並直接地分析出圖像的語意訊息，且不需經由影像資料庫檢索的方式完成語意編碼，進而表達出影像的含意。本研究的動機和目的在於結合語意式影像檢索技術和內容式影像檢索技術，以減少低階特徵和高階語意特徵的差距，讓兩者能夠相輔相成，以提升影像檢索的成效。我們還使用自組織映射圖(SOFM)類神經網路技術對資料庫影像作分群，如此不僅可以大幅提升影像檢索的效率，而且當有新影像加入資料庫時，只需比對少數的影像，即可將新影像作分類。經過實驗的結果顯示，結合影像的高階語意特徵和低階特徵後，的確有提升影像檢索的效果。

Due to the progress of information technology as well as the popularity of internet and multimedia, the data amount has been increased tremendously in the last decade, where images are the most commonly used data in multimedia. In order to analyze and manage the huge image data efficiently, people need to use computer to create image files and analyze these image data. Using suitable features to describe the image data, users can browse and retrieve these image data conveniently.
The semantic message of images can assist effective retrieval of images. Therefore, the key points are how to effectively analyze the semantic message of images directly, how to encode semantic message not by retrieving image database, and how to demonstrate the meaning of images. This paper presents an approach that combines semantic-based image retrieval with content-based image retrieval in order to decrease the gap between low-level features and high-level semantic feature. Using the SOFM neural network technology, we can classify the images in database. Therefore, we can not only enhance the efficiency of image retrieval, but also can add new images into classes with only few image comparisons. The experimental results reveal that, after combining the high-level semantic feature and the low-level features of the image, the image retrieval can be improved with the classification of images.

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