大規模之影像物件搜尋系統

Large-Scale Image Object Retrieval by Efficient Query Evaluation and Effective Auxiliary Visual Feature Discovery

郭盈希(Yin-Hsi Kuo)

指導教授 ： 徐宏民

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2010年)

https://doi.org/10.6342/NTU.2010.10165

摘要

無資料

關鍵字

影像物件檢索 ； 反轉索引 ； 雜湊索引 ； 查詢擴展 ； 查詢評估

並列摘要

Image object retrieval aims to retrieve database images in which image object query might only cover a small region. Object retrieval over a large-scale database is challenging because of the need to respond user query efficiently; existing methods suffer from low recall rate since target images differ visually from the query due to commonly observed occlusions, scaling, lighting variations, etc. Therefore, the success of an image object retrieval system hinges on the following issues: 1) finding the occurrences of specific objects in the image collections; 2) responding to queries over the million-scale collections in less than few seconds; 3) improving recall rate. To resolve these issues, we propose efficient query evaluation with pseudo-objects in the adapted inverted indexing to achieve time efficiency. In addition, we propose to augment each image with auxiliary visual features from image (textual and visual) clusters by informative feature selection and propagation, automatically mined offline in a distributed platform (i.e., MapReduce). Experiments show that the proposed framework takes 121ms for a query in the one-million collection; it achieves significant improvement in retrieval accuracy (e.g., up to 98.8% relative improvement) over the prior methods.

並列關鍵字

image object retrieval ； locality sensitive hashing (LSH) ； inverted file ； query expansion ； query evaluation

參考文獻

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