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首页> 外文期刊>Journal of visual communication & image representation >Boosting image object retrieval and indexing by automatically discovered pseudo-objects
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Boosting image object retrieval and indexing by automatically discovered pseudo-objects

机译：通过自动发现的伪对象来增强图像对象的检索和索引编制

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摘要

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. An image object 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. To tackle this problem, we propose a novel representation, pseudo-objects - a subset of proximate feature points with its own feature vector to represent a local area, to approximate candidate objects in database images. In this paper, we investigate effective methods (e.g., grid, G-means, and GMM-BIC) to estimate pseudo-objects. Additionally, we also confirm that the pseudo-objects can significantly benefit inverted-file indexing both in accuracy and efficiency. Experimenting over two consumer photo benchmarks, we demonstrate that the proposed method significantly outperforms other state-of-the-art object retrieval and indexing algorithms.
机译：最新的对象检索系统主要基于可视包表示，该表示将特征图像中图像的局部外观信息编码。通过将查询对象的特征向量与数据库图像的特征向量进行比较来执行图像对象搜索。然而，数据库图像向量通常携带整个图像的混合信息，该图像可能包含多个对象和背景。这种嘈杂的（或稀释的）特征向量会降低搜索质量。为了解决这个问题，我们提出了一种新颖的表示形式，即伪对象-近似特征点的子集，它具有自己的特征向量来表示局部区域，以近似数据库图像中的候选对象。在本文中，我们研究了估算伪对象的有效方法（例如，网格，G均值和GMM-BIC）。此外，我们还确认，伪对象可以在准确性和效率上极大地受益于反向文件索引。在两个消费者照片基准测试中，我们证明了该方法明显优于其他最新的对象检索和索引算法。

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