近年來，影像檢索技術因為數位影像及多媒體資料庫的快速成長，而顯得愈來愈重要，也因為這些資料量非常龐大，所以管理這些資料的工具也愈顯得重要。這些檢索方法大部份採用的是兩個步驟的方法：第一步是將資料庫□的影像以所謂的特徵向量來代表每個影像；第二步是將要查詢的影像以同樣的步驟取得代表它的特徵向量，然後和資料庫□中影像的特徵向量比對，藉此找出相近的影像。
我們的方法結合了顏色及紋理來做影像擷取，且我們可以和使用者互動，讓使用者選擇較重要的部份來做比對。在使用到的技術方面，我們使用色彩來做群集分類，這是一個很直接且方便的方法；另外凌波轉換在近年來被大量的運用，像是JPEG 2000的標準就是使用凌波轉換來做壓縮的技術，而複數凌波轉換乃是最近才被提出來，利用這個技術，我們可以更快速的得到我們所想要的紋理的特徵向量。

我們提出的方法亦是使用兩個步驟的方法，在第一步□，首先我們把資料庫內的影像利用每個點的色彩採用群集分類的方法，把一張影像分成好幾個部份，再對每個部份用複數凌波轉換取得它們的特徵向量；在第二步□，我們針對所要查詢的影像做上述同樣的動作，再把所算出來的特徵向量和資料庫中的影像的特徵向量比對，藉以找出最相近的影像。

In recent years, image indexing techniques have become important with the rapid growth of digital images and video libraries. As the volume of image information grows, the management tools must become increasingly reliable. A two steps approach to search the image database is adopted. First for each image in the database, a feature vector characterizing some image properties is computed. Second, given a query image, its feature vector is computed too, compared to the feature vectors stored in the database, and try to find out the most similar images to the query image.
Our work combines color and texture for image retrieval, and we allow user interaction. Wavelet transform has become more and more popular, some standards like JPEG 2000 is based upon wavelet transform instead of DCT. A new complex wavelet transform gives a fast way to generate texture features.

In our method, there are two steps to retrieve image . First, we segment the image into several clusters by the color using C-Means algorithm and apply complex wavelet transform to each cluster to compute the feature vector and store the feature vector in out feature database. Second, we apply the same process to our query image and compute the feature vector. Then we compare the feature vector with those stored in the feature database to find out the most similar images.

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