为图像理解铺平道路--一种新的图像分解方法
Paving the Way for Image Understanding: A New Kind of Image Decomposition is Desired
摘要：
在本文中，我们提出了一种非常规的图像分割方法，旨在满足图像理解和模式识别任务的要求。图像理解通常包括图像信息内容发现和图像信息内容解释两个子过程。尽管它的广泛使用，“图像信息内容”的概念仍然是不明确的，直观的，和模糊的。最常见的是，它是在香农的意义上使用，这意味着在整个信号集合上平均的信息内容评估。然而，人类很少求助于这种估计。它们非常有效地将图像分解为有意义的成分，并将注意力集中到感知相关的图像部分。我们认为，在人类注意力视觉研究的最新发现和Kolmogorov的复杂性理论的概念，可以提出一种非正统的分割方法，提供有效的图像分解，以保持图像片段的信息，非常适合随后的图像判读。我们提供了一些说明性的例子，证明这种方法的有效性。
英文摘要：
In this paper we present an unconventional image segmentation approach which is devised to meet the requirements of image understanding and pattern recognition tasks. Generally image understanding assumes interplay of two sub-processes: image information content discovery and image information content interpretation. Despite of its widespread use, the notion of "image information content" is still ill defined, intuitive, and ambiguous. Most often, it is used in the Shannon's sense, which means information content assessment averaged over the whole signal ensemble. Humans, however,rarely resort to such estimates. They are very effective in decomposing images into their meaningful constituents and focusing attention to the perceptually relevant image parts. We posit that following the latest findings in human attention vision studies and the concepts of Kolmogorov's complexity theory an unorthodox segmentation approach can be proposed that provides effective image decomposition to information preserving image fragments well suited for subsequent image interpretation. We provide some illustrative examples, demonstrating effectiveness of this approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
