Research on Trademark Image Retrieval Technology based on Shape Matching

随着市场经济的发展，商标数量逐年递增。传统的基于分类、文本标注的商标图像检索方法存在着很大的难题，包括手工分类/注解工作量大、描述主观性、描述不全面性等问题。基于内容的图像检索技术可以克服这些弊端，它在商标检索领域得到了非常广泛的应用。 基于内容的商标图像检索方法利用图像自身包含的特征属性，如颜色、形状、纹理及空间位置关系等建立图像的索引，然后利用这些特征进行检索。作为人工图像的商标图像，其形状特征较其它特征更为显著，人们往往更多地通过形状来识别不同的商标。本文主要针对基于形状匹配的商标图像检索关键问题展开研究，包括：商标图像分割技术、形状边界描述方法、形状区域描述方法、形状特征融合及匹配技...With the development of market economy, the quantity of trademark increases progressively year by year. The tradition trademark image retrieval based on classified or text labeling has many problems, including the very load of manual illustration work, subjectivity, inaccuracy, etc. Content-based image retrieval (CBIR) technology can overcome these problems, so it obtained the very widespread appl...学位：理学博士院系专业：信息科学与技术学院计算机科学系_哲学学号：2005140320

Improwed Detection Technology of Video Cars in Complex Scenes

视频运动目标检测是数字视频处理、分析应用的一个重要领域,其目的是把作为一个整体的视频图像序列,通过一定的方法挖掘出具有意义的运动实体数据。该文对传统阈值法的缺陷进行分析,采用改进的二维阈值结合遗传的方法提高求解寻优的速度和效率,并通过帧差结合背景补偿的方式,提出一种适合于在复杂背景环境下实时检测运动车辆的新方法。实验结果表明,该方法有较强的环境适应能力,能够很好地检测出运动车辆。In the digital video signal processing,the technology of video moving object detection is very important.Mining meaning moving objects from a video image sequence by a certain method is its intention.In this paper,according to the analysis of the deficiency of the traditional threshold method,an improved 2D gray-level histogram combined with genetic algorithm is applied to enhance the speed and efficiency.A new method based on adaptive background subtraction and frame difference for the real-time detection of moving cars in complex scenes is proposed.Experimental results show that the new method has better environmental adaptive ability and can detect moving cars well.厦门大学“985工程”2期基金资助项目(0000-X07204);; 厦门大学校科研基金资助项目(0630-X01117

A New Cluster Validity Index for Fuzzy Clustering

本文提出了一个在模糊聚类中判别聚类有效性的新指标。该指标可有效地对类间有交叠或有多孤立点的情况做出准确的判定。文中基于模糊C-均值聚类算法(FCM),应用多组的测试数据对其进行了性能分析,并与当前较广泛使用且较具代表性的某些相关指标进行了深入的比较。实验结果表明,该指标函数的判定性能是优越的,它可以自动地确定聚类的最佳个数。In this paper, we propose a new validity index for determining the number of clusters. It is based on a novel way of combining cohesion and discrepancy. Extensive tests of the index in a conventional model selection process (FCM algorithm) have been performed using generated data sets and public domain data sets,and comparison with several existing and important indices has been made. The results obtained show clearly the efficiency of the new index under the condition of overlapping clusters.姜青山(教授)校科研启动基金(0630-X01117

Shape Representation and Similarity Measure Based on Delaunay Triangulation

在计算机视觉中,形状的表示和相似性衡量是重要且复杂的问题,提出了一种改进的SUSAN(最小一致性区域)拐点检测算法并用于形状表示,同时基于Delaunay三角化给出了一个用于形状相似性衡量的有效算法。首先,对形状的拐点进行Delaunay三角形构造,然后从Delaunay三角网中获得Delaunay图矩阵,最后使用矩阵的谱对拐点进行匹配。在含有1 400幅图像的MPEG-7 CE-Shape-1数据库中的检索实验进一步验证了算法的有效性。Shape representation and similarity measure are important and difficult problems in computer vision and have been extensively studied for decades.This paper presents an enhanced SUSAN(Smallest Univalue Segment Assimilating Nucleus) Corner Detector for shape representation and an effective algorithm to establish shape similarity measure based on Delaunay triangulation.Firstly,delaunay triangulation was constructed among corners of each shape which has been normalized in advance.Secondly,the Delaunay graph matrix was achieved from Delaunay triangulation net.Finally,the corners were matched by using spectrum of the graph matrix.Shape retrieval Experiments have been conducted on the MPEG-7 Core Experiment CE-Shape-1 database of 1 400 images which illustrate good performance of the algorithm.National 985 Project(0000-X07204);National 863 Plan(2006AA01Z129

Hierarchical Color Image Segmentation Using Watershed Filling and Overlap-rate Measuring

由于分水岭方法进行图像分割时经常是在梯度图像上进行,并经常产生过分割的结果,因此为克服图像过分割问题和提高分割的准确性,提出了一种基于分水岭和重叠率衡量分层融合策略的彩色图像分割新算法——HWO。该算法首先将RGB颜色空间转化到Lab颜色空间,并根据a、b维来提取统计2维直方图,同时在直方图上运用分水岭分割方法,通过对峰进行填充来得到图像的初步分割结果;然后将与填充对应的分割区域样本与高斯分布结合起来,对图像进行高斯混合模型假设下的参数估计;最后对模型与模型间进行重叠率衡量及分层区域融合,以得到最终的图像分割结果。实验中,首先采用训练图像集对算法涉及的两个参数进行确定,然后对测试图像集的分割效果和分割时间性能进行评估,评估是以标准的人工分割图像库为基准的。实验结果表明,该算法可解决过分割问题,其评估所得分准率及分全率综合衡量系数为0.609,而人工分割综合衡量系数为0.79,同时新方法的分割时间仅为传统方法的1/3,分割速度有了较大提高。Watershed segmentation based on gradient images usually has over-segmentation result.To solve over-segmentation problem,we propose a new Hierarchical image segmentation method based on Watershed filling and Overlap-rate measuring(HWO).Firstly,we transform RGB color space to Lab and statistic the histogram according to a and b dimensions.The watershed segmentation algorithm is applied to 2D histogram and the initial segmentation result is achieved.Then,we associate the segmentation region with the Gaussian distributing,and estimate the parameter value.Finally,we measure the Overlap-rate for a hierarchical region merging and get the final result.In the experiment,the two parameters are determined.We then evaluate the segmentation performance with a standard database of human segmented natural images.Results show our method can efficiently solve over-segmentation problem,and the combined value of precision and recall measures is 0.609,while is 0.79 when the segmentation is done manually.In addition,the new method also has much less computing complexity.教育部“211”计划“985”工程-2期项目(000-X07204);; 国家高技术研究发展计划(863)项目(2006AA01Z129

HSEC:基于聚类的启发式选择性集成

提出一种基于聚类的启发式选择性集成学习算法.集成学习通过组合多个弱分类器获得比单一分类器更好的学习效果,把多个弱分类器提升为一个强分类器.理论上来说弱分类器的个数越多,组合的模型效果越好,但是随着弱分类器的增多,模型的训练时间和复杂度也随之递增.通过聚类的方法去除相似的弱分类器,一方面有效降低模型的复杂度,另一方面选出差异性较大的弱分类器作为候选集合.之后采用启发式的选择性集成算法,对弱分类器进行有效的组合,从而提升模型的分类性能.同时采用并行的集成策略,提高集成学习选取最优分类器子集效率,可以有效地减少模型的训练时间.实验结果表明,该算法较传统方法在多项指标上都有着一定的提升.国家自然科学基金(31200769