A GA-based Recommender Strategy for Image Retrieval

• Main Authors: Tsun-Wei Chang, 張遵偉
• Other Authors: Yo-Ping Huang
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/yue38m

博士 === 大同大學 === 資訊工程學系(所) === 95 === Along with the advanced information technologies, the availability of World Wide Web together with the rapid growth of photographic archives have attracted our research motivation in providing an efficient access to the digital image database through browsing and searching. Content-based image retrieval (CBIR) has been intensively studied in the last decades. In this dissertation, some existing CBIR systems and related literatures are reviewed, and the focusing issues of retrieval strategies are addressed. We emphasize the topics of effective image segmentation, fast image retrieval model and efficient relevance feedback mechanism. This dissertation proposes an efficient genetic algorithm-based image retrieval strategy that applies the regions of interest and relevance feedback mechanism to improve the retrieval efficiency. Three main contributions have been achieved. (1) A fuzzy inference model is presented to derive an effective image segmentation method. A set of higher order statistical descriptors are used to represent the characteristics of a region content. (2) A GA-based image retrieval model is proposed. To assist the users to formulate more precise queries, the proposed system allows users to choose specific regions from multiple images. According to the human preference, the combination of image content descriptors from the selected regions forms the chromosomes of the genetic algorithm used for retrieving the target images. (3) The user relevance feedback mechanism is employed to direct the advanced search. The selected regions by a user are transformed into a transaction record. Furthermore, the retrieval performance is further improved by mining association rules from the retrieval feedback. The system architecture and methodology are detailed in this dissertation and thorough experiments on different queries demonstrate the effectiveness and scalability of the proposed strategy. Meanwhile, the prospects of future research directions and topics are also given in the conclusion chapter.