基于计算机视觉的目标图像检索相关技术的研究
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摘要
基于计算机视觉的目标图像检索是计算机视觉领域中最具有挑战性的研究主题之一，其主要内容是利用计算机模拟人的视觉来描述图像的内容，并根据内容描述的特征从海量的图像中找出感兴趣的目标图像。它在网络图像搜索、医学图像挖掘、基于内容的视频检索、安全监控和不良图像过滤等领域得到了广泛的应用，并且是机器学习、模式识别、计算机视觉和图像处理等多门学科交叉研究的热点与难点。由于它的复杂性和结构性，使得基于计算机视觉的目标图像检索相关技术的研究面临众多挑战，图像检索的准确性尚有待提高。
本文围绕着基于计算机视觉的目标图像检索进行了一系列的研究，结合多尺度空间理论、视觉显著性理论和视觉一致性理论等方法进行深入研究，对实际场合下的应用给出了切实可行的方案，例如光照变化下的目标图像检索、基于视觉一致性的目标图像检索等，针对检索过程中的特征提取、检索模型和视觉显著性问题，提出了相应的解决方法，其主要的研究内容与创新点如下：
1．特征提取的过程中，由于采集过程中条件的变化和噪声的干扰，导致目标图像发生角度变化、光照变化，从而提取出的纹理特征发生变化，这时基于局部二元模式LBP (Local Binary Pattern)纹理特征的图像检索方法很可能将会失效。针对这一问题，提出一种基于彩色空间LBP局部纹理特征的目标图像检索方法。彩色空间LBP特征实际上是将简化的LBP特征和彩色空间特征表示方法相结合，在保留了原有LBP局部特征的旋转不变性同时，也增加了光照不变性。此外原有的LBP特征丢弃了彩色信息是为了减少特征维度以提高检索速度，本文提出的彩色空间映射方法可以减少特征维度，从而降低方法的计算复杂度。实验结果表明该方法对复杂的光照变化、角度变化和尺度变化下的目标图像可以更有效地进行分类识别与检索。
2．特征提取出来后，用于图像检索的过程中，一个标注出目标物体区域的图像有助于提高目标图像检索的准确率，传统方法是通过人为分割出目标物体用于训练分类器，但是对于大容量的图像数据库来说，这种人工标注方式是不可行的。针对这一问题，提出一种基于多尺度视觉显著性的目标图像检索方法。该算法适用于背景并不复杂且不存在遮挡的无监督的目标图像检索。在训练阶段利用统计学习来训练一个多尺度目标显著性检测模型，然后利用该模型自适应地提取出训练图像中的显著性区域，提取显著性区域的颜色、亮度和方向特征，最后结合概率潜在语义分析(PLSA)模型用于目标图像检索。实验结果表明文中方法可以自动地检测出图像中的显著目标的区域，通过目标显著性重排序后，能够有效地改进搜索引擎返回的图像结果的准确率。
3．图像搜索引擎的返回结果往往不令人满意，常出现与检索主题不符的图像。这是因为当局部噪声图像显著性比真实图像更高时，噪声图像就成为了检索结果中排序较高的图像。针对这一问题，提出一种集成视觉一致性和目标显著性的目标图像检索方法，从图像搜索引擎的结果中发掘出视觉一致性模式。该方法首先计算搜索引擎返回结果的目标显著图，利用目标显著系数进行初始过滤；然后对集合内所有图像的显著目标求取视觉一致性模式；最后根据视觉一致性进行图像聚类。实验结果证明该方法不仅可以提高图像检索结果的准确性，还可以有效地提升图像搜索引擎的检索性能，将具有视觉显著性的且与查询主题密切相关的图像优先返回给用户。
Computer vision-based object image retrieval is one of the most challenging researchtopic in the field of computer vision, Its main content is how to use computer simulation ofhuman vision to describe the content of the image, and identifying a target image of interestfrom the mass of the image according to the features of the content description. It has beenwidely used in network image search, medical image mining, content-based video retrieval,security monitoring, and pornography image filtering, and is hot and difficult interdisciplinaryresearch in machine learning, pattern recognition, computer vision and image processing. Dueto its complexity and structural, the object image retrieval based on computer visiontechnology still has many challenges, and its accuracy needs to be improved.
This paper conducts a series of research on computer vision-based image retrieval, andcombines multi-scale space theory, object significance theory and vision conformity theory topropose feasible scheme for the actual image retrieval application, such as the object imageretrieval of illumination changes, the object image retrieval based on visual consistency andso on. This paper is focused on the feature extraction of the retrieval process, retrieval modeland visually significant problem, and proposes the corresponding solution. Its main researchcontent and innovation are as follows:
In feature extraction process, due to the acquisition of conditions change and noiseinterference, the angle variation of object image occurs and illumination changes, theextracted texture features will change, and the image retrieval method based on LBP (LocalBinary Pattern) texture characteristics will probably lapse. To solve this problem, this paperproposes an object image retrieval method based on LBP local texture features of color space.LBP features of color space are actually the combination of simple LBP feature and colorspace feature. It retains the original LBP local feature rotation invariance, and increase theillumination invariant too. In addition, the original LBP features discarding color informationis to reduce the dimension of the feature to improve the retrieval speed. This paper proposes acolor space mapping method which can reduce the feature dimension, and then reduces thecomputational complexity. The experimental results show that object images of complexillumination changes, angle transform and scale changes can be more effectively used forclassification by this method.
In the process of feature extraction and image retrieval, a marked out image of objectregion can be helped to improve the accuracy of the target image retrieval. The traditionalmethod is through artificial division of object to train the classifier, but for large capacity image database, such artificial labeling method is not feasible. To solve this problem, wepropose an object image retrieval method based on multi-scale visual saliency. This method issuitable for unsupervised object image retrieval method with simple background and noshelter. At first, we use statistical learning to train a multi-scale target saliency detectionmodel, and then use the model to adaptively extract the significant region in the trainingimage, so we extract the color, intensity and direction of significant area.Finally, we combinethese feature with Probabilistic Latent Semantic Analysis (PLSA) model for the object imageretrieval. The experimental results show that the proposed method can automatically detectthe significant target region in the image, and be effective to improve the accuracy of imageretrieval system by the object significant feature sorting.
The results returned from image retrieval system often appear with the unrelated images,and that is not satisfactory. This is because when the significance of local noise images arehigher than the true images, the noise images will become higher ones of image retrievalresults in sort-priority order.To solve this problem, we propose an image retrieval methodscombined with vision conformity and object significance by mining the vision conformityfrom results of image retrieval system. Firstly, we calculate the object saliency maps fromthe results of image retrieval system, and use objective significant coefficients for the initialfilter, and them we strike a vision conformity mode with all the significant object in thecollection images. Finally, we make image clustering according to vision conformity.Experimental results show that this method can not only improve the accuracy of imagesearch results, but also effectively enhance performance of image retrieval system, and givepriority to these images which have visual significance and are closely related to the querytopics image back to the user.
本文围绕着基于计算机视觉的目标图像检索进行了一系列的研究，结合多尺度空间理论、视觉显著性理论和视觉一致性理论等方法进行深入研究，对实际场合下的应用给出了切实可行的方案，例如光照变化下的目标图像检索、基于视觉一致性的目标图像检索等，针对检索过程中的特征提取、检索模型和视觉显著性问题，提出了相应的解决方法，其主要的研究内容与创新点如下：
1．特征提取的过程中，由于采集过程中条件的变化和噪声的干扰，导致目标图像发生角度变化、光照变化，从而提取出的纹理特征发生变化，这时基于局部二元模式LBP (Local Binary Pattern)纹理特征的图像检索方法很可能将会失效。针对这一问题，提出一种基于彩色空间LBP局部纹理特征的目标图像检索方法。彩色空间LBP特征实际上是将简化的LBP特征和彩色空间特征表示方法相结合，在保留了原有LBP局部特征的旋转不变性同时，也增加了光照不变性。此外原有的LBP特征丢弃了彩色信息是为了减少特征维度以提高检索速度，本文提出的彩色空间映射方法可以减少特征维度，从而降低方法的计算复杂度。实验结果表明该方法对复杂的光照变化、角度变化和尺度变化下的目标图像可以更有效地进行分类识别与检索。
2．特征提取出来后，用于图像检索的过程中，一个标注出目标物体区域的图像有助于提高目标图像检索的准确率，传统方法是通过人为分割出目标物体用于训练分类器，但是对于大容量的图像数据库来说，这种人工标注方式是不可行的。针对这一问题，提出一种基于多尺度视觉显著性的目标图像检索方法。该算法适用于背景并不复杂且不存在遮挡的无监督的目标图像检索。在训练阶段利用统计学习来训练一个多尺度目标显著性检测模型，然后利用该模型自适应地提取出训练图像中的显著性区域，提取显著性区域的颜色、亮度和方向特征，最后结合概率潜在语义分析(PLSA)模型用于目标图像检索。实验结果表明文中方法可以自动地检测出图像中的显著目标的区域，通过目标显著性重排序后，能够有效地改进搜索引擎返回的图像结果的准确率。
3．图像搜索引擎的返回结果往往不令人满意，常出现与检索主题不符的图像。这是因为当局部噪声图像显著性比真实图像更高时，噪声图像就成为了检索结果中排序较高的图像。针对这一问题，提出一种集成视觉一致性和目标显著性的目标图像检索方法，从图像搜索引擎的结果中发掘出视觉一致性模式。该方法首先计算搜索引擎返回结果的目标显著图，利用目标显著系数进行初始过滤；然后对集合内所有图像的显著目标求取视觉一致性模式；最后根据视觉一致性进行图像聚类。实验结果证明该方法不仅可以提高图像检索结果的准确性，还可以有效地提升图像搜索引擎的检索性能，将具有视觉显著性的且与查询主题密切相关的图像优先返回给用户。
Computer vision-based object image retrieval is one of the most challenging researchtopic in the field of computer vision, Its main content is how to use computer simulation ofhuman vision to describe the content of the image, and identifying a target image of interestfrom the mass of the image according to the features of the content description. It has beenwidely used in network image search, medical image mining, content-based video retrieval,security monitoring, and pornography image filtering, and is hot and difficult interdisciplinaryresearch in machine learning, pattern recognition, computer vision and image processing. Dueto its complexity and structural, the object image retrieval based on computer visiontechnology still has many challenges, and its accuracy needs to be improved.
This paper conducts a series of research on computer vision-based image retrieval, andcombines multi-scale space theory, object significance theory and vision conformity theory topropose feasible scheme for the actual image retrieval application, such as the object imageretrieval of illumination changes, the object image retrieval based on visual consistency andso on. This paper is focused on the feature extraction of the retrieval process, retrieval modeland visually significant problem, and proposes the corresponding solution. Its main researchcontent and innovation are as follows:
In feature extraction process, due to the acquisition of conditions change and noiseinterference, the angle variation of object image occurs and illumination changes, theextracted texture features will change, and the image retrieval method based on LBP (LocalBinary Pattern) texture characteristics will probably lapse. To solve this problem, this paperproposes an object image retrieval method based on LBP local texture features of color space.LBP features of color space are actually the combination of simple LBP feature and colorspace feature. It retains the original LBP local feature rotation invariance, and increase theillumination invariant too. In addition, the original LBP features discarding color informationis to reduce the dimension of the feature to improve the retrieval speed. This paper proposes acolor space mapping method which can reduce the feature dimension, and then reduces thecomputational complexity. The experimental results show that object images of complexillumination changes, angle transform and scale changes can be more effectively used forclassification by this method.
In the process of feature extraction and image retrieval, a marked out image of objectregion can be helped to improve the accuracy of the target image retrieval. The traditionalmethod is through artificial division of object to train the classifier, but for large capacity image database, such artificial labeling method is not feasible. To solve this problem, wepropose an object image retrieval method based on multi-scale visual saliency. This method issuitable for unsupervised object image retrieval method with simple background and noshelter. At first, we use statistical learning to train a multi-scale target saliency detectionmodel, and then use the model to adaptively extract the significant region in the trainingimage, so we extract the color, intensity and direction of significant area.Finally, we combinethese feature with Probabilistic Latent Semantic Analysis (PLSA) model for the object imageretrieval. The experimental results show that the proposed method can automatically detectthe significant target region in the image, and be effective to improve the accuracy of imageretrieval system by the object significant feature sorting.
The results returned from image retrieval system often appear with the unrelated images,and that is not satisfactory. This is because when the significance of local noise images arehigher than the true images, the noise images will become higher ones of image retrievalresults in sort-priority order.To solve this problem, we propose an image retrieval methodscombined with vision conformity and object significance by mining the vision conformityfrom results of image retrieval system. Firstly, we calculate the object saliency maps fromthe results of image retrieval system, and use objective significant coefficients for the initialfilter, and them we strike a vision conformity mode with all the significant object in thecollection images. Finally, we make image clustering according to vision conformity.Experimental results show that this method can not only improve the accuracy of imagesearch results, but also effectively enhance performance of image retrieval system, and givepriority to these images which have visual significance and are closely related to the querytopics image back to the user.
引文
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