基于内容的图像检索综述

本文简要介绍了基于内容的图像检索，给出了基于内容的图像检索系统的一般结构。对图像检索的发展进行了概述。对基于内容的图像检索的主要研究技术进行了详细和全面的论述，并介绍了几个典型的基于内容的图像检索系统。最后，指出了目前研究中存在的一些主要问题。     

基于内容的图像检索技术与医学图像检索

在分析基于内容的图像检索技术特点的基础上，提出了4种基于内容的图像检索方法，并对每种方法的实现特别是特征抽取进行了一定的研究。根据医学图像的使用特点，对基于内容的医学图像检索技术进行了初步的研究；对医学图像特征的抽取，应将重点放在形状特征和纹理特征的抽取上；同时，对医学图像进行检索，还可以使用颜色空间分布特征，来进一步进行相似匹配。     

图像检索中的关键技术

传统的基于数值／字符的信息检索技术并不能满足海量的图像检索要求，因此，基于内容的图像检索技术（CBIR，content-based Image retrieval）得到了广泛研究，本主要讨论CBIR研究中的一些关键问题：图像的内容特征及其提取，特征之间的相似度计算，查询条件的表达等，最后指出了一些可值得深入研究的方向。     

基于小波多尺度分析的彩色图像检索方法

多媒体技术的普及和Internet技术的实施导致了大量图像信息的出现，基于文本关键词的传统检索方法已不能适应图像信息检索的要求，这使得基于内容的图像检索技术逐渐成为目前的研究热点。基于内容检索技术中必不可少的关键步骤就是图像特征的提取，其中可提取的特征有颜色、纹理和形状等。但是，由于图像的每种特征只能抓住图像相似性的某一个方面，因此如何能更好地表示图像就成为基于内容图像检索中一个重要的研究方向。针对该问题，提出了一种基于图像颜色和纹理特征的图像检索方法，其中颜色特征采用HSV颜色空间的直方图，纹理特征采用图像小波多尺度表示方法中细节信息的方差统计量，这样就充分利用了颜色的丰富表现性和小波变换的多分辨性及其变换系数的统计特性。通过对不同类型图像使用不同特征组合进行图像检索查准率的对比实验结果表明，这种图像检索方法是行之有效的。     

基于内容的图像检索系统研究

对基于内容的图像检索系统进行了详细的分析研究，重点对颜色、纹理和形状等图像可视特征的提取和表示进行了研究，并简要介绍了基于内容的图像检索中图像语义的处理和应用，最后提出了CBIR系统及其相关技术的发展趋势和研究重点。     

基于内容的图像检索介绍

基于内容特征的图象检索（CBIR）是目前国内外研究的一个热点。本文简要介绍了基于内容的图像检索技术的发展过程及主要原理，重点论述了基于内容的图像检索常用关键技术——图像视觉特征的描述和提取。     

Internet上的图像检索技术

图像数据的无序激增使得图像检索技术的成为一个研究热点。介绍了当前Internet上图像检索的几种常用的技术，如关键字检索、基于内容的检索、矢量图形(W3C制定的SVG标准)的检索，并对每种技术的优点与缺点做了分析，同时介绍了当前Internet上图像检索技术的研究热点并对其未来的发展做了一个展望。     

基于内容特征的图像数据库检索技术及实现

随着图像数据库容量的增大,迫切的需要提高对数据库内图像进行检索的准确率和效率,基于图像内容特征的检索技术发展是一个重要的提高检索效率的途径。本文在分析了基于内容特征的图像检索技术基础上,针对多维索引技术发展及图像数据库的特点,提出了一种新的改进NB-Tree的基于颜色特征的图像检索技术,通过引入新的信息特征矢量,实现了检索效率的提高,并给出了一个具体的实例验证了技术的正确性。     

基于内容的图像检索技术研究

对于一个图像检索系统,最重要的是信息获取的准确性、快速性和有效性。通过对现有基于内容的图像检索技术的分析和比较,指出了现有基于内容的图像检索系统的难点和不足。同时指出了基于内容的图像检索技术的发展趋势和研究方向。     

基于内容的图像检索系统

文章介绍了基于内容的图像检索技术及其优势，并举了几个例子。     

Feature engineering for semantic place prediction

We present in this paper our winning solution to Dedicated Task 1 in Nokia Mobile Data Challenge (MDC). MDC Task 1 is to infer the semantic category of a place based on the smartphone sensing data obtained at that place. We approach this task in a standard supervised learning setting: we extract discriminative features from the sensor data and use state-of-the-art classifiers (SVM, Logistic Regression and Decision Tree Family) to build classification models. We have found that feature engineering, or in other words, constructing features using human heuristics, is very effective for this task. In particular, we have proposed a novel feature engineering technique, Conditional Feature (CF), a general framework for domain-specific feature construction. In total, we have generated 2,796,200 features and in our final five submissions we use feature selection to select 100 to 2000 features. One of our key findings is that features conditioned on fine-granularity time intervals, e.g. every 30 min, are most effective. Our best 10-fold CV accuracy on training set is 75.1% by Gradient Boosted Trees, and the second best accuracy is 74.6% by L1-regularized Logistic Regression. Besides the good performance, we also report briefly our experience of using F# language for large-scale (～70 GB raw text data) conditional feature construction.     

基于积分投影的人脸图像的特征提取

人脸识别是模式识别领域内的重要课题，有着十分广泛的应用前景，人脸特征的自动提取是人脸自动识别过程中重要的一步。该文采用基于人脸几何特征的方法，首先通过边缘检测和阈值技术对人脸图像进行预处理；然后分别采用水平和垂直积分投影的方法确定人脸轮廓，最后利用人脸特征的先验知识，提取出特征点。实验结果表明该人脸特征提取系统能有效地提取头部轮廓和人脸的主要特征点，实现简单，效率高，特别适合于标准证件类型的黑白照的识别。     

用矩特征进行光字符识别的一种新方法

模式识别是信息科学及其应用中的一个非常活跃的领域,各种特征提取及识别方法是层出不穷。本文在分析已有特征提取技术的弊端的基础上,提出用规格化后的中心矩作为特征向量的新技术,并将其应用于光字符识别中。规格化后的中心矩具有平移不变性和伸缩不变性,因而提高了识别率。在本光字符识别系统中,给出了矩特征向量的选取方法并确定了分类策略。实验表明,当使用8或10行字符的特征向量进行判别时,可得到超过95％的识别率。     

Improved feature reduction in input and feature spaces

In this paper, we present an improved feature reduction method in input and feature spaces for classification using support vector machines (SVMs). In the input space, we select a subset of input features by ranking their contributions to the decision function. In the feature space, features are ranked according to the weighted support vector in each dimension. By applying feature reduction in both input and feature spaces, we develop a fast non-linear SVM without a significant loss in performance. We have tested the proposed method on the detection of face, person, and car. Subsets of features are chosen from pixel values for face detection and from Haar wavelet features for person and car detection. The experimental results show that the proposed feature reduction method works successfully. In fact, our method performs better than the methods of using all the features and the Fisher's features in the detection of person and car. We also gain the advantage of speed.     

文本分类中一种基于正交变换的特征降维方法

本文讨论了一种基于正交变换的文本特征降维方法.分析了基于特征选择和特征抽取的特征降维方法各自特点,借助矩阵的分解论证了基于Fisher准则函数的特征降维模式的原理与理论基础,讨论了PCA与SVD两种模式的相互关系.实验结果表明这种特征降维模式在文本分类的准确性方面效果较好.     

Pedestrian Re-ID based on feature consistency and contrast enhancement

For robust person re-identification(Re-ID), the key is effectively learning the features of body parts and their long-distance dependence. ResNet and Transformer are respectively good at learning local dependence and long-distance dependence between region features due to their respective special structures. In order to fully integrate the advantages of the two models, we propose a novel person Re-ID framework that effectively incorporates pixel-level region features, posture-level relation features and the long-distance dependence of region features. Specifically, we design a Semantic Correction Module (SCM) that corrects pixel-level region features and posture-level relation features in a masked manner to generate discriminative fine-grained features with high pose semantics. Considering the semantic inconsistency between relation features and region features, we propose a Contrastive Association Module (CAM) to interactively enhances the long-distance correlation and local saliency of features in a self-attention way. Finally, to improve the robustness of local and global features, we construct a CAM layer to enhance the representation of features based on their potential relationships. Extensive experiment results on general and occlusion datasets demonstrate that our approach performs favorably against the state-of-the-art methods, e.g. 96% Rank-1 on Market-1501.     

图像检索系统构建中的几个问题

本文对综合利用图像的内容特征和图像元特征以及将图像特征的自动提取和手工提取相结合,采用分布式结构和高效的查询接口,从而构建高效实用的图像检索系统中的一些问题进行了探讨.     

Boosting decision stumps for dynamic feature selection on data streams

Feature selection targets the identification of which features of a dataset are relevant to the learning task. It is also widely known and used to improve computation times, reduce computation requirements, and to decrease the impact of the curse of dimensionality and enhancing the generalization rates of classifiers. In data streams, classifiers shall benefit from all the items above, but more importantly, from the fact that the relevant subset of features may drift over time. In this paper, we propose a novel dynamic feature selection method for data streams called Adaptive Boosting for Feature Selection (ABFS). ABFS chains decision stumps and drift detectors, and as a result, identifies which features are relevant to the learning task as the stream progresses with reasonable success. In addition to our proposed algorithm, we bring feature selection-specific metrics from batch learning to streaming scenarios. Next, we evaluate ABFS according to these metrics in both synthetic and real-world scenarios. As a result, ABFS improves the classification rates of different types of learners and eventually enhances computational resources usage.     

Generalized K-L transform based combined feature extraction

In this paper, we combine two kinds of features together by virtue of complex vectors and then use the developed generalized K-L transform (or expansion) for feature extraction. The experiments on NUST603 handwritten Chinese character database and CENPARMI handwritten digit database indicate that the proposed method can improve the recognition rate significantly.     

Feature-based crystal construction in computer-aided nano-design

Providing nanoengineers and scientists efficient and easy-to-use tools to create geometry conformations that have minimum energies is highly desirable in materials design. Recently we developed a periodic surface model to assist the construction of nanostructures parametrically for computer-aided nano-design. In this paper, we present a feature-based approach for crystal construction. The proposed approach creates models of basic features with the aid of periodic surfaces followed by operations between basic features. The goal is to introduce a rapid construction method for complex crystal structures.