以擬似物件提昇物件搜尋效能

Abstract

目前的物件搜尋系統多以視覺文字為基礎為影像建立包含區域特徵的特徵向量，並以向量空間中各特徵向量間的距離或相似度為基準進行搜尋的動作。然而，這樣的特徵向量雖然考慮到區域特徵，但因為以單一特徵向量代表一張影像，所以當影像中有多個物件以及背景時，各個物件的資訊便混而為一，該影像的特徵向量因此是混雜的且稀釋過的，無法代表影像中任何物件，導致物件搜尋效果不佳。我們提出擬似物件的概念，用以近似影像中的實際物件，並解決前述的問題。影像中的一個擬似物件是該影像在空間中有鄰近關係的特徵點子集合，每一子集合會有相對應的特徵向量，代表其涵蓋的局部區域，因此每張影像會有多個特徵點子集合，即多個擬似物件來代表影像中實際可能包含的物件。我們提出三種有效估計出擬似物件的方法，分別是格狀方法（Grid），G-means方法，和高斯混合模型-貝氏訊息準則方法（GMM-BIC）。我們在兩個大眾影像資料集進行物件搜尋實驗，證明所提出的三個方法的效能遠超過目前的物件搜尋系統。

State-of-the-art object retrieval systems are mostly based on the bag-of-visual-words representation which encodes local appearance information of an image in a feature vector. A search is performed by comparing query object’s feature vector with those for database images. However, a database image vector generally carries mixed information of the entire image which may contain multiple objects and background. Search quality is degraded by such noisy (or diluted) feature vectors. We address this issue by introducing the concept of pseudo-objects to approximate candidate objects in database images. A pseudo-object is a subset of proximate feature points in an image with its own feature vector to represent a local area. We investigate effective methods (e.g., grid, G-means, and GMM-BIC) to estimate pseudo-objects. Experimenting over two consumer photo benchmarks, we demonstrate the proposed method significantly outperforming other state-of-the-art object retrieval algorithms.