基于Bag-of-Features算法的车辆检测研究

车辆检测已成为交通运输工程(ACC)和先进辅助驾驶系统(ADAS)中的核心技术之一。该算法利用车辆的边缘特征与Bag-of-Features(BoF)模型的融合对前方运动车辆进行实时检测,主要包含车辆假设存在区域生成和假设区域验证两部分。首先,对图像进行预处理后利用Sobel边缘检测处理得到车辆假设存在的区域;然后,利用Bag-of-Features的K最近邻域算法对假设存在区域进行验证。该算法与其他算法最大的区别在于将边缘和Bag-of-Features相结合来提高检测率。通过对实际道路视频进行测试,结果表明,该方法能够实时准确地检测出道路上前方运动车辆。

基于局部形状特征和Bag-of-Feature模型的磨粒图像形状特征提取

以磨粒图像为研究对象,提出了基于局部形状特征和Bag-of-Features模型的磨粒形状特征提取方法。首先构建磨粒区域的骨架,根据骨架端点和分支得到磨粒的轮廓基元和区域基元,不同数量的相邻的轮廓和区域基元组合构成局部轮廓和局部区域,提取局部轮廓和区域的形状特征,两者融合得到磨粒的局部形状特征集合。然后根据Bag-of-Features模型的思想,以训练集所有磨粒样本的局部形状特征集合为基础,构建视觉词典,经过特征编码、特征汇集和归一化,得到磨粒形状特征的编码向量表示。最后根据形状特征的编码向量,采用多级支持向量机的方法对磨粒类型进行识别。实验结果表明,基于提出的磨粒形状特征方法能够有效、准确地识别磨粒类型。

基于特征联合和直方图交叉核函数的动作识别方法

为提高动作识别的识别率和实时性,提出了一种新颖的基于特征联合和直方图交叉核函数的动作识别方法。该方法首先跟踪视频中运动物体上的局部时空特征点形成运动轨迹,并计算出轨迹的梯度方向直方图(HOG)、光流直方图(HOF)、运动边界直方图(MBH)特征和轨迹上各点所在视频帧局部区域的局部二值模式(LBP)特征组成联合特征矩阵;然后等量地对每种动作的各训练样本的联合特征矩阵进行平均采样,将采样结果合并后运用bag-of-features方法进行K-means聚类形成码书,在此基础上利用码书量化各样本的联合特征矩阵得到表示视频样本中运动信息及结构信息的特征向量;最后将形成的特征向量作为支持向量机(SVM)的输入,同时选择直方图交叉核函数作为SVM的核函数,训练动作识别的分类器并进行测试。实验结果表明,该方法不仅提高了动作识别的识别率,而且通过利用直方图交叉核函数可缩短分类器的训练与测试时间。

基于条件随机场的多标签图像分类

利用Bag-of-feature方法将图像语义近似的转化为一维序列,使用二阶条件随机场将图像的底层语义特征和高层知识关联起来,识别图像内容,并通过对多个初始特征不同的条件随机场推理结果的组合学习来提高图像内容识别的准确率,从而提高分类精度。标准图像数据集的实验证明,该方法有效关联了标签语义,取得了更好的多标签分类效果。

基于压路机多角度判别的倾轧轨迹面检测算法

为实时监控压路机的填压覆盖情况,提出一种基于压路机多角度判别的倾轧轨迹面自动检测算法。提出BOF-MCF(bag-of-feature-matched correlation filter)特征参数,实现压路机角度的自动检测。针对不同角度下的压路机,提出多角度压路机ROI粗定位及其轮廓二级自动分割算法,结合LSD(line segment detector),自动检测压路机碾轮轴长/架长,将帧间图像的碾轮轴长/架长进行叠加计算,得到压路机倾轧轨迹面。实验对国家电网施工现场的视频数据进行测试,其结果表明,该算法对压路机的角度识别正确率达到90%以上,对倾轧轨迹面识别的正确率为83.00%-92.72%。

Plant Identification Using Fitness-Based Position Update in Whale Optimization Algorithm

Since the beginning of time,humans have relied on plants for food,energy,and medicine.Plants are recognized by leaf,flower,or fruit and linked to their suitable cluster.Classification methods are used to extract and select traits that are helpful in identifying a plant.In plant leaf image categorization,each plant is assigned a label according to its classification.The purpose of classifying plant leaf images is to enable farmers to recognize plants,leading to the management of plants in several aspects.This study aims to present a modified whale optimization algorithm and categorizes plant leaf images into classes.This modified algorithm works on different sets of plant leaves.The proposed algorithm examines several benchmark functions with adequate performance.On ten plant leaf images,this classification method was validated.The proposed model calculates precision,recall,F-measurement,and accuracy for ten different plant leaf image datasets and compares these parameters with other existing algorithms.Based on experimental data,it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%.

基于特征直方图的蛋白质结构分类和检索方法

针对蛋白质结构的自动分类需求,提出一种基于BOVF方法的蛋白质结构分类与检索新算法。该算法利用SURF提取蛋白质结构各视角图像的局部特征,利用K-means对所有特征进行聚类分析生成视觉词典,统计视角图像的特征在视觉词典中出现的频率并生成特征直方图,以此直方图作为蛋白质结构的特征,使用KLD计算2个结构之间的相似度。3个实验分别被用来检验本文算法的分类和检索性能。实验结果证明本文算法具有比较算法无法达到的分类效果和检索性能。

High-dimensional indexing technologies for large scale content-based image retrieval: a review

The boom of Internet and multimedia technology leads to the explosion of multimedia information, especially image, which has created an urgent need of quickly retrieving similar and interested images from huge image collections. The content-based high-dimensional indexing mechanism holds the key to achieving this goal by efficiently organizing the content of images and storing them in computer memory. In the past decades, many important developments in high-dimensional image indexing technologies have occurred to cope with the 'curse of dimensionality'. The high-dimensional indexing mechanisms can mainly be divided into three categories: tree-based index, hashing-based index, and visual words based inverted index. In this paper we review the technologies with respect to these three categories of mechanisms, and make several recommendations for future research issues.

Integrating semantic features in fruit recognition based on perceptual color and semantic template

Many fruit recognition works have applied statistical approaches to make an exact correlation between low-level visual feature information and high-level semantic concepts givenby predefined text caption or keywords. Two common fruit recognition models include bagof-features (BoF) and convolutional neural network (ConvNet), which achieve highperformance results. In most cases, the overfitting problem is unavoidable. This problemmakes it difficult to generalize new instances with only a slightly different appearance,although belonging to the same category. This article proposes a new fruit recognitionmodel by associating an object’s low-level features in an image with a high-level concept.We define a perceptual color for each fruit species to construct a relationship between fruitcolor and semantic color name. Furthermore, we develop our model by integrating the perceptual color and semantic template concept to solve the overfitting problem. The semantic template concept as a mapping between the high-level concept and the low-level visualfeature is adopted in this model. The experiment was conducted on three different fruitimage datasets, with one dataset as train data and the two others as test data. The experimental results demonstrate that the proposed model, called perceptual color on semantictemplate (PCoST), is significantly better than the BoF and ConvNet models in reducing theoverfitting problem.