圖像搜索結果的重疊層次聚類與代表點展現(編輯修改稿)
2025-08-19 10:49 本頁面
【文章內容簡介】 er. this method utilized image distance to measure similarity and identified overlapping clusters by using . as a result, an image may be partitioned into multiple clusters, and this partition makes each cluster more specific pared with others. to validate this method, experiments were carried out on the datasets returned by search engine when searching for some key words. the result shows that the proposed method can find explicit clusters with partial overlapping.key words:image clustering。 link clustering。 partition。 image distance隨著圖像數量的急劇增長，圖像聚類［19］已成為將大量圖像劃分為少數有意義分組(簇)的重要技術。通常情況下，通過圖像搜索引擎返回的搜索結果包含多個主題。將結果組織為不同語義的簇有利于用戶的瀏覽。然而，對于圖像聚類存在很多挑戰(zhàn)，例如高維災難、可伸縮性差、準確度低、簇意義模糊、簇部分重疊、圖像關鍵特征不易提取等。本文關注的問題是如何對web圖像的搜索結果
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