基于多尺度块局部二值模式的金字塔直方图场景分类

Q4 Computer Science
Dipankar Das
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引用次数: 5

摘要

提出了一种用于场景分类识别的多尺度块局部二值模式(PH-MBLBP)描述符金字塔直方图。我们表明,场景分类,特别是室内和室外环境,需要其视觉描述符来处理与其他视觉域不同的属性(例如,用于对象分类的SIFT描述符)。我们提出的PH-MBLBP满足这些特性,适合场景分类任务。由于所提出的PH-MBLBP主要对图像模式的微观和宏观结构进行编码,因此,它比基本LBP算子提供了相对更完整的图像描述符。此外,我们的PH-MBLBP描述符是比传统算子更强大的纹理描述符,它的计算速度也非常快。我们的实验表明,PH-MBLBP优于其他描述符,如SIFT。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scene Classification Using Pyramid Histogram of Multi-Scale Block Local Binary Pattern
Pyramid Histogram of Multi-scale Block Local Binary Pattern (PH-MBLBP) descriptor for recognizing scene categories, is presented in this paper. We show that scene categorization, especially for indoor and outdoor environments, requires its visual descriptor to process properties that are different from other vision domains (e.g., SIFT descriptor used for object categorization). Our proposed PH-MBLBP satisfies these properties and suits the scene categorization task. Since the proposed PH-MBLBP mainly encodes micro- and macro-structures of image patterns, thus, it provides relatively more complete image descriptor than the basic LBP operator. Moreover, our PH-MBLBP descriptor is more powerful texture descriptor than the conventional operator and it can also be calculated extremely fast. Our experiments demonstrate that PH-MBLBP outperforms the other descriptor such as SIFT.
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来源期刊
International Journal of Computer Science and Applications
International Journal of Computer Science and Applications Computer Science-Computer Science Applications
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期刊介绍: IJCSA is an international forum for scientists and engineers involved in computer science and its applications to publish high quality and refereed papers. Papers reporting original research and innovative applications from all parts of the world are welcome. Papers for publication in the IJCSA are selected through rigorous peer review to ensure originality, timeliness, relevance, and readability.
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