| Summary: | 碩士 === 國立臺灣科技大學 === 電機工程系 === 104 === With the advance of multimedia technology and communications, images and videos become the major streaming information through the Internet. How to fast retrieve desired similar images precisely from the Internet scale image/video databases (Big Data) is the most important retrieval control target. In this paper, a cloud based content-based image retrieval (CBIR) scheme is presented. To speed up the features matching process for large scale CBIR, we proposed to perform Database-Categorizing based on Weighted-Inverted Index (DCWII) and Database Filtering Algorithm (DFA). In the DCWII, it assigns weights to DCT coefficients histograms and categorizes the database by weighted features. In addition, the DFA filters out irrelevant image in the database to reduce unnecessary computation loading for features matching. Experiments showed that the proposed CBIR scheme outperforms previous works in the Precision-Recall performance and maintains mean average precision (mAP) about 0.678 in the large-scale database comprising one mega images. Our scheme also can reduce about 55%~70% retrieval time by pre-filtering the database, which helps to improve efficiency of retrieval system.
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