Multi-scale local structure patterns histogram for describing visual contents in social image retrieval systems
Multi-scale local structure patterns histogram for describing visual contents in social image retrieval systems
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DOI：
10.1007/s11042-016-3436-9
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发表时间：
2016-10-01
影响因子：
3.6
通讯作者：
Baik, Sung Wook
中科院分区：
文献类型：
--
作者：
Ahmad, Jamil;Sajjad, Muhammad;Baik, Sung Wook
基于内容的图像检索系统严重依赖于从图像中提取的特征集。有效的图像表示成为此类系统中的关键一步。视觉内容表示的一个关键挑战是减少所谓的“语义差距”。现有的方法无法以人性化的方式描述内容。受人类视觉系统启发的内容表示方法在图像检索中显示出了有希望的结果。在过去的二十年里，人们在开发受人类视觉系统启发的提取描述符的方法方面进行了大量的工作，并试图根据用户的需求有效地检索视觉内容，从而减少语义差距。尽管在这一领域进行了广泛的研究，但当前图像检索系统的局限性仍然存在。本文提出了一种个性化社交图像集合的描述符，该描述符利用多个尺度图像的显着边缘图中的局部结构模式。人类视觉系统在基本层面上对边缘、角落、交叉点和生成局部结构图案的图像中的其他此类强度变化敏感。在多个尺度上分析这些模式可以捕获最显着的细粒度和粗粒度特征。这些特征累积在局部结构模式直方图中以索引图像，从而允许灵活查询视觉内容。检索结果表明，所提出的描述符在大型社交图像集合的类似最先进方法中排名靠前。
Content based image retrieval systems rely heavily on the set of features extracted from images. Effective image representation emerges as a crucial step in such systems. A key challenge in visual content representation is to reduce the so called 'semantic gap'. It is the inability of existing methods to describe contents in a human-oriented way. Content representation methods inspired by the human vision system have shown promising results in image retrieval. Considerable work has been carried out during the past two decades for developing methods to extract descriptors inspired by the human vision system and attempt to retrieve visual contents efficiently according to the user needs, thereby reducing the semantic gap. Despite the extensive research being conducted in this area, limitations in current image retrieval systems still exist. This paper presents a descriptor for personalized social image collections which utilizes the local structure patterns in salient edge maps of images at multiple scales. The human visual system at the basic level is sensitive to edges, corners, intersections, and other such intensity variations in images generating local structure patterns. Analyzing these patterns at multiple scales allow the most salient fine-grained and coarse-grained features to be captured. The features are accumulated in a local structure patterns histogram to index images allowing flexible querying of visual contents. The retrieval results show that the proposed descriptor ranks well among similar state-of-the-art methods for large social image collections.