商标检索中形状特征描述的研究

针对图像检索中的＂语义鸿沟＂问题,以商标图像为研究对象。分析了拉丁字母的物象、抽象和意象等含义,构建了一套＂字母描述子＂语言。同时还分析了商标的起源、构成和特征,并提出了用＂字母描述子＂对商标图像进行特征描述和编码的方法。经系列的检索结果和统计数据说明,用＂字母描述子＂对商标特征进行描述、编码和检索的方法具有一定的实用意义。     

基于语言编码的商标记忆规律研究

目的 研究商标视觉信息中所包含的语言要素,分析人们在接受视觉输入时,大脑激活的语言编码对长时记忆产生的影响,探索利用语言编码加深人们对商标记忆的途径,为设计更具辨识度、记忆度的商标提供思路。方法 以心理学中对语言编码和视觉编码的研究为基础,对人在识记图像时产生的语言编码进行分类,分析视觉编码与语言编码相互影响与转化的方式,并结合实验来分析不同类型的语言编码在人脑记忆商标时的优先级,总结语言编码在记忆商标时的积极作用和不利影响,最后通过实际案例表明语言编码判断商标相似性争议时的优势。结果 在商标的长时记忆与回忆再现过程中,均需要语言编码协助视觉编码来完成。结论 在商标设计过程中,不仅要考虑图形的美观性与独特性,还要考虑受众在识记过程中形成的语言编码的逻辑与复杂度,在推广品牌时兼顾消费者的认知共识性与模糊指向性,从而提高品牌商标的辨识度。     

综合PHOG和LWT的图像检索算法

目的研究物体的形状特征在图像描述及图像检索中的区分度和检索性能。方法设计一种综合PHOG形状和提升小波变换的图像检索算法。算法首先对原始图像进行极坐标系方向归一化,提取图像旋转不变特征;其次提取分层图像的PHOG形状特征;然后提取分层图像低频变换系数均值和方差作为提升小波变换特征;最后将各种特征进行融合并用于图像检索,并定义距离衡量公式。结果通过文中设计算法提取的图像形状特征可使各标准测试图像间距离均值为0.2352。结论在Corel图像集上的检索实验结果优于RIM算法和FWTH算法,表明文中算法图像检索领域具有一定的应用前景。     

Medical image registration‐based retrieval using distance metrics

In this article, registration and retrieval are carried out separately for medical images and then registration‐based retrieval is performed. It is aimed to provide a more thorough insight on the use of registration, retrieval, and registration‐based retrieval algorithm for medical images. The purpose of this work is to deal these techniques with anatomical imaging modalities for clinical diagnosis, treatment, intervention, and surgical planning in a more effective manner. Two steps are implemented. In the first step, the affine transformation‐based registration for medical image is processed. The second step is the retrieval of medical images processed by using seven distance metrics such as euclidean, manhattan, mahalanobis, canberra, bray‐curtis, squared chord, chi‐squared, and also by using the features like mean, standard deviation, skewness, energy, and entropy. Now images registered by affine transformation are applied for retrieval. In this work, both registration and retrieval techniques in medical domain share some common image processing steps and required to be integrated in a larger system to complement each other. Experimental results, it is evident that euclidean and manhattan produces 100% precision and 35% recall found to have higher performance in retrieval. From the four anatomical modalities considered (brain, chest, liver, and limbs) brain image has better registration. It is also found that though the registration of images changes the orientation, for better performance of images in clinical evaluation it does not widely affect the retrieval performance. In the medical domain the ultimate aim of this work is to provide diagnostic support to physicians and radiologists by displaying relevant past cases, along with proven pathologies as ground truth from experimental results. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 360–371, 2013     

基于泽尼克矩耦合颜色空间加权度量的商标检索算法

目的 针对商标检索系统中利用单一特征进行识别和度量时,往往难以充分表征商标特征,易出现检索精度和鲁棒性不高等问题,文中拟设计一种泽尼克（Zernike）矩耦合颜色空间加权度量的商标检索方案。方法 首先,利用Zernike矩作为商标的形状描述符,充分描述商标的形状信息。随后,利用颜色空间来描述图像中像素空间信息的颜色分布特征。然后,分别将输入商标的Zernike矩特征、颜色空间特征与存储在数据库中的特征进行匹配,以计算Zernike矩特征的加权Euclidean距离与颜色空间度量。最后,联合颜色空间度量与Euclidean距离,综合考虑形状与颜色特征,形成新的距离测量规则,输出与查询商标相似的商标。结果 实验数据表明,与当前商标检索算法相比较,所提算法具有更高的检索准确率与鲁棒性,表现出更为理想的Precision-Recall以及平均准确率（Mean Average Precision, MAP）。结论 所提算法返回的图像与查询图像相似度较高,在商标注册、侵权保护等方面中具有一定的参考价值。     

基于多尺度特征变换与颜色相关性的商标检索算法

目的 提出一种快速有效的商标注册相似性检查方法,以解决当前基于SIFT的商标检索系统易出现漏检、误检,导致检索精度不高的问题。方法 首先,利用SIFT进行尺度空间创建,并检测商标的特征关键点,通过确定关键点的主方向,可得到具有旋转、缩放、平移、视图变化不变性的图像形状特征描述符。随后,根据像素与其邻域的颜色和空间位置,定义一种改进的颜色相关性,为了有效避免不同商标可能具有相似的颜色特征,对不同的颜色赋予一个权重因子,从而得到一个反映颜色空间相关性与颜色排布疏密度的颜色特征。然后,将SIFT与颜色相关特征向量进行加权组合,并根据实际过程中占主导作用的特征来改变权重。最后,根据加权组合特征,引入马氏距离对查询商标与数据库商标进行相似度量,输出检索商标。结果 实验结果表明,与当前先进的商标检索系统对比,所提算法具有更高的检索准确性与效率。结论 所提算法具有良好的检索准确率与鲁棒性,在商标注册等领域具有一定的实用价值。     

基于轨迹包络的雕塑实体特征描述

本文提出了用“轨迹包络法”来构造雕塑型体表面，该方法能有效地记录被雕表面的几何信息，保留并继承雕塑艺术风格。     

A content-based image retrieval system based on object-moment feature

The last decade has witnessed great interest in research on content-based image retrieval (CBIR). In 2009, Lin et al. proposed a smart CBIR system based on colour and texture feature. Their system has a high detection rate except the cases where image objects have similar shapes. To enhance the detection rate a shape-based image feature called object-moment is proposed in this paper. Object-moment uses the moment of force to compute the object edge feature by calculating the distance from each edge pixel to the axis, and adding them up as a feature. Besides, we integrate the colour features (NSOM, CSOM) and the texture features (CCM, DBPSP) to enhance image detection rate and simplify computation of image retrieval. A series of analyses and comparisons are performed in our experiments to demonstrate that our proposed method improves the retrieval accuracy significantly.