基於MPEG壓縮描述子用於視覺化檢索(CDVS)之架構,本論文提出一個利用本文資訊,濾掉特徵點配對中錯誤配對的方法。在區域描述子量化以及配對缺乏幾何資訊下,特徵點配對很容易產生非常多的錯誤,影響幾何驗證的判斷。因此,本論文嘗試結合了半區域演算法和虛弱幾何一致性兩方法,有效的將錯誤的配對濾除,進而減少幾何驗證的誤判率。為了減輕本文確認自身複雜度,本論文提出了以本文資訊為基礎之適配度檢定(WGC Goodness-of-fit Test),有效將近一半的雜訊影像省略幾何驗證的步驟。最後,在觀察到特徵點配對才是檢索中最複雜之下,本論文亦提出兩種以雜湊為基礎的特徵點配對方法,有效地降低整體檢索架構時間,並維持檢索精確度。實驗結果發現在mAP上相對於CDVS有1.0-2.1%的進步,而就檢索時間的分析上,本文資訊適配度檢定可以節省約4-14%的時間,而雜湊特徵點配對可以節省將近40-50%的時間。
A key problem in MPEG-7 Compact Descriptors for Visual Search (CDVS) framework is the ambiguity of feature matching. To alleviate it, a new contextual verification scheme is introduced in CDVS by combining semi-local constraints and weak geometric consistency check. To mitigate time complexity incurred by the contextual verification, we propose a goodness-of-fit test based on the features’ orientation, which is motivated by the CDVS goodness-of-fit test. Moreover, we propose two hash-based feature matching schemes to speed up the feature matching process, which is found to be the most time-consuming process in the current CDVS framework. Experimental results show that the contextual verification offers 1.0-2.1% mAP improvements over CDVS. For time reduction experiments, 4-14% and 40-50% time savings are achieved by our goodness-of-fit test and hash-based feature matching scheme.
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