Enhanced Gradient-Based Local Feature Descriptors by Saliency Map for Egocentric Action Recognition

Egocentric video analysis is an important tool in healthcare that serves a variety of purposes, such as memory aid systems and physical rehabilitation, and feature extraction is an indispensable process for such analysis. Local feature descriptors have been widely applied due to their simple implementation and reasonable efficiency and performance in applications. This paper proposes an enhanced spatial and temporal local feature descriptor extraction method to boost the performance of action classification. The approach allows local feature descriptors to take advantage of saliency maps, which provide insights into visual attention. The effectiveness of the proposed method was validated and evaluated by a comparative study, whose results demonstrated an improved accuracy of around 2%

Ensemble of different local descriptors, codebook generation methods and subwindow configurations for building a reliable computer vision system

Abstract In the last few years, several ensemble approaches have been proposed for building high performance systems for computer vision. In this paper we propose a system that incorporates several perturbation approaches and descriptors for a generic computer vision system. Some of the approaches we investigate include using different global and bag-of-feature-based descriptors, different clusterings for codebook creations, and different subspace projections for reducing the dimensionality of the descriptors extracted from each region. The basic classifier used in our ensembles is the Support Vector Machine. The ensemble decisions are combined by sum rule. The robustness of our generic system is tested across several domains using popular benchmark datasets in object classification, scene recognition, and building recognition. Of particular interest are tests using the new VOC2012 database where we obtain an average precision of 88.7 (we submitted a simplified version of our system to the person classification-object contest to compare our approach with the true state-of-the-art in 2012). Our experimental section shows that we have succeeded in obtaining our goal of a high performing generic object classification system. The MATLAB code of our system will be publicly available at http://www.dei.unipd.it/wdyn/?IDsezione=3314&IDgruppo_pass=124&preview= . Our free MATLAB toolbox can be used to verify the results of our system. We also hope that our toolbox will serve as the foundation for further explorations by other researchers in the computer vision field

AdaBoost 방법을 통해 학습된 SVM 분류기를 이용한 영상 분류

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 유석인.This thesis presents the algorithm that categorizes images by objects contained in the images. The images are encoded with bag-of-features (BoF) model which represents an image as a collection of unordered features extracted from the local patches. To deal with the classification of multiple object categories, the one-versus-all method is applied for the implementation of multi-class classifier. The object classifiers are built as the number of object categories, and each classifier decides whether an image is included in the object category or not. The object classifier has been developed on the AdaBoost method. The object classifier is given by the weighted sum of 200 support vector machine (SVM) component classifiers. Among multiple object classifiers, the classifier with the highest output function value finally determines the category of the object image. The classification efficiency of the presented algorithm has been illustrated on the images from Caltech-101 dataset.Abstract i Contents iii List of Figures v List of Tables vi Chapter 1 Introduction 1 Chapter 2 Related Work 3 2.1 Image classification approaches . . . . . . . . . . . 3 2.2 Boosting methods . . . . . . . . . . . . . . . 6 2.3 Background . . . . . . . . . . . . . . . . . 9 2.3.1 Support vector machine . . . . . . . . . . . . . 9 Chapter 3 Proposed Algorithm 12 3.1 SIFT feature extraction . . . . . . . . . . . . . 13 3.2 Codebook construction . . . . . . . . . . . . . 15 3.3 Bag-of-features representation . . . . . . . . . . . 16 3.4 Classifier design . . . . . . . . . . . . . . . 16 Chapter 4 Experiments 20 4.1 Dataset . . . . . . . . . . . . . . . . . . 20 4.2 Bag-of-features representation . . . . . . . . . . . 22 4.3 Classifiers . . . . . . . . . . . . . . . . . 24 4.4 Classification results . . . . . . . . . . . . . . 25 Chapter 5 Conclusion 29 Bibliography 30 Abstract in Korean 34Maste

Using Poselet and Scene Information for Action Recognition in Still Images

提出一种结合姿态特征和场景信息对图像中的人体行为进行分类的方法,采用多尺度密集采样和SIfT特征对图像进行特征提取和描述,以非参数概率密度估计方法对特征空间的样本分布进行估计,并对概率密度梯度向量在码本单词上的聚集进行描述得到紧凑且有判别力的场景编码.姿态分类则利用人体部位的表观和配置关系从图像中提取出与特定行为类别相关的姿态特征,利用最大分类间隔姿态分类器计算得到每个测试样本属于各个行为类别的评分值.最后结合姿态分类器和行为场景分类器两种分类器输出值完成对测试样本的分类.将本文的方法运用于WIllOW-ACTIOnS数据集上进行评价,实验结果证明了该方法的有效性.This paper proposes a novel method for action recognition in still images using a combination of poselet and scene information.First,multi-scale dense sampling and SIFT descriptor are applied in feature extraction and description.Then non-parametric probability density estimation is employed to estimate the spatial distribution of feature space.To obtain discriminative scene feature,the gradient of probability density function is calculated and the vectors aggregation on visual words of action codebook is described for scene based action classification.While for pose classification,action-specific appearance and configuration patterns of human body part are extracted from training images,then a set of pose classifiers are trained to evaluate the class label confidence which test samples belongs to.Finally,the outputs of scene classifier and pose classifier are combined to decide the final class label.We validate our approach on Willow-action dataset and experimental results showthat it achieves superior performance in comparison to several baseline methods.国家自然科学基金项目(61373076;61202143)资助; 高校博士学科点专项科研基金项目(20110121110024)资助; 中央高校基本科研业务费专项资金(11QZR04)资助; 福建省大数据重点实验室开放课题(2014KL03)资助; 华侨大学科研启动经费(13BS409)资

Locally Linear Salient Coding for image classification

Representing images with their descriptive features is the fundamental problem in CBIR. Feature coding as a key-step in feature description has attracted the attentions in recent years. Among the proposed coding strategies, Bag-of-Words (BoW) is the most widely used model. Recently saliency has been mentioned as the fundamental characteristic of BoW. Base on this idea, Salient Coding (SaC) has been introduced. Empirical studies show that SaC is not able to represent the global structure of data with small number of codewords. In this paper, we remedy this limitation by introducing Locally Linear Salient Coding (LLSaC). This method discovers the global structure of the data by exploiting the local linear reconstructions of the data points. This knowledge in addition to the salient responses, provided by SaC, helps to describe the structure of the data even with a few codewords. Experimental results show that LLSaC obtains state-of-the-art results on various data types such as multimedia and Earth Observation