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Content-Based Image Retrieval: From the Object Detection/Recognition Point of View

Content-Based Image Retrieval: From the Object Detection/Recognition Point of View

Ming Zhang, Reda Alhajj
Copyright: © 2009 |Pages: 30
ISBN13: 9781605661742|ISBN10: 1605661740|ISBN13 Softcover: 9781616925635|EISBN13: 9781605661759
DOI: 10.4018/978-1-60566-174-2.ch006
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MLA

Zhang, Ming, and Reda Alhajj. "Content-Based Image Retrieval: From the Object Detection/Recognition Point of View." Artificial Intelligence for Maximizing Content Based Image Retrieval, edited by Zongmin Ma, IGI Global, 2009, pp. 115-144. https://doi.org/10.4018/978-1-60566-174-2.ch006

APA

Zhang, M. & Alhajj, R. (2009). Content-Based Image Retrieval: From the Object Detection/Recognition Point of View. In Z. Ma (Ed.), Artificial Intelligence for Maximizing Content Based Image Retrieval (pp. 115-144). IGI Global. https://doi.org/10.4018/978-1-60566-174-2.ch006

Chicago

Zhang, Ming, and Reda Alhajj. "Content-Based Image Retrieval: From the Object Detection/Recognition Point of View." In Artificial Intelligence for Maximizing Content Based Image Retrieval, edited by Zongmin Ma, 115-144. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-174-2.ch006

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Abstract

Content-Based Image Retrieval (CBIR) aims to search images that are perceptually similar to the querybased on visual content of the images without the help of annotations. The current CBIR systems use global features (e.g., color, texture, and shape) as image descriptors, or usefeatures extracted from segmented regions (called region-based descriptors). In the former case, descriptors are not discriminative enough at the object level and are sensitive to object occlusion or background clutter, thus fail to give satisfactory result. In the latter case, the features are sensitive to the image segmentation, which is a difficult task in its own right. In addition, the region-based descriptors are still not invariant to varying imaging conditions. In this chapter, we look at the CBIR from the object detection/recognition point of view and introduce the local feature-based image representation methods recently developed in object detection/recognition area. These local descriptors are highly distinctive and robust to imaging condition change. In addition to image representation, we also introduce the other two key issues of CBIR: similarity measurement for image descriptor comparison and the index structure for similarity search.

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