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7 June 2004 Can the high-level content of natural images be indexed using local analysis?
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Abstract
Early methods of image indexing relied heavily on color histograms, which characterize the global content of images. However, global indexing methods proved to be unsatisfactory, and researchers now employ more localized measures of image content, based on relatively small regions. At the same time, it has also become clear that image indexing should be based on higher-level visual content. This raises an important question: “Can the higher-level content of images be reliably indexed using local analysis?” In general, humans are better at indexing mid-level and high-level visual content than today’s automated indexing algorithms. Therefore, it makes sense to ascertain how well humans can perform midlevel or high-level indexing, based on small regions. This paper describes research that employs a set of outdoor scenery images (called the NaturePix image set) to compare how successfully humans can label the visual content of small regions of natural images when (1) these regions are seen in the context of the larger image, and (2) when these regions are extracted from (and are seen in isolation from) that larger image. The results of these experiments indicate what types of higher-level image content can be recognized locally, and how successfully high-level image content can be indexed on the basis of local feature analysis.
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John Arthur Black Jr., Mariano Phielipp, Greg Nielson, and Sethuraman Panchanathan "Can the high-level content of natural images be indexed using local analysis?", Proc. SPIE 5292, Human Vision and Electronic Imaging IX, (7 June 2004); https://doi.org/10.1117/12.527316
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