Trainable classifier-fusion schemes: An application to pedestrian detection

Ludwig, Oswaldo; Delgado, David; Gonçalves, Valter; Nunes, Urbano

doi:10.1109/itsc.2009.5309700

Cited by 122 publications

(53 citation statements)

References 11 publications

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“…We have also calculated the results by taking the square root of the feature vector (Power Kernel) before applying linear kernel. We found the HOG descriptor shows an average of 78.61% accuracy when combining it with SPM structure (we use the default parameter settings of the source code: http://www.mathworks.com/matlabcentral/fileexchange/28689-hog-descriptor-for-matlab, and as described in [9]). It is noteworthy to mention here that, without using SPM, the accuracy is only 61.22%.…”

Section: Resultsmentioning

confidence: 99%

“…For texture based classification, there are several existing well known methods such as Wavelets transform [18], Gabor filters [19], Scale-invariant feature transform (SIFT) [20], HOG [9,15], LBP [7] features. Recently, LBP is considered as an effective texture classification methodology which was proposed for describing the local structure of an image.…”

Section: Texture Based Classificationmentioning

confidence: 99%

“…To identify the design class of a garment product, it is necessary to analyze the texture of the product. For texture classification, there are several existing well known methods such as CENsus Transform hiSTogram (CENTRIST) [6], Local Binary Pattern (LBP) [7], Gabor [8], Histogram of Oriented Gradient (HOG) [9], GIST [10] etc. Among those, LBP and CENTRIST are very similar and gain popularity for their computational simplicities and better accuracies.…”

Section: Introductionmentioning

confidence: 99%

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An Automated System for Garment Texture Design Class Identification

2015

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Automatic identification of garment design class might play an important role in the garments and fashion industry. To achieve this, essential initial works are found in the literature. For example, construction of a garment database, automatic segmentation of garments from real life images, categorizing them into the type of garments such as shirts, jackets, tops, skirts, etc. It is now essential to find a system such that it will be possible to identify the particular design (printed, striped or single color) of garment product for an automated system to recommend the garment trends. In this paper, we have focused on this specific issue and thus propose two new descriptors namely Completed CENTRIST (cCENTRIST) and Ternary CENTRIST (tCENTRIST). To test these descriptors, we used two different publically available databases. The experimental results of these databases demonstrate that both cCENTRIST and tCENTRIST achieve nearly about 3% more accuracy than the existing state-of-the art methods.

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Section: Resultsmentioning

confidence: 99%

Section: Texture Based Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Automated System for Garment Texture Design Class Identification

2015

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“…The patches are given to the Histogram of Oriented Gradients (HOG) descriptor and the extracted feature vectors are used to calculate the codebook as well as the cluster activities. In order to compute the HOG feature vector [35], [36], the HOG descriptor divides each patch into smaller regions known as blocks, η×η. The HOG descriptor computes two gradients (horizontal gradient h x and vertical gradient h y ) with respect to every coordinate x, y of an image using and a simple edge detector (kernel gradient detector) [37].…”

Section: Bag Of Visual Words With Histogram Of Oriented Gradients (Homentioning

confidence: 99%

Comparative study between deep learning and bag of visual words for wild-animal recognition

Okafor

Pawara

Karaaba

et al. 2016

2016 IEEE Symposium Series on Computational Intelligence (SSCI)

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Abstract-Most research in image classification has focused on applications such as face, object, scene and character recognition. This paper examines a comparative study between deep convolutional neural networks (CNNs) and bag of visual words (BOW) variants for recognizing animals. We developed two variants of the bag of visual words (BOW and HOG-BOW) and examine the use of gray and color information as well as different spatial pooling approaches. We combined the final feature vectors extracted from these BOW variants with a regularized L2 support vector machine (L2-SVM) to distinguish between classes within our datasets. We modified existing deep CNN architectures: AlexNet and GoogleNet, by reducing the number of neurons in each layer of the fully connected layers and last inception layer for both scratch and pre-trained versions. Finally, we compared the existing CNN methods, our modified CNN architectures and the proposed BOW variants on our novel wild-animal dataset (Wild-Anim). The results show that the CNN methods significantly outperform the BOW techniques.

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“…현재 대부분의 행동인식 방법들은 다양한 local descriptor를 융합하는데 초점을 두며 하나의 판별 모델을 사용하 였다. 그러나 최근 보행자 검출이나 [8] 영상 분류 [9] 를 위하여 다수개의 판별 모델을 사용하여 영상 인식 성능을 향상시 킨 연구가 많이 진행 되었다. 이러한 방법들의 특징은 다양 한 특성의 판별 모델을 사용함으로써 서로 상호보완적 효 과를 얻을 수 있기 때문에 판별 성능을 향상 시킬 수가 있었 다.…”

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A Study for Improved Human Action Recognition using Multi-classifiers

Kim¹,

Ro²

2014

Journal of Broadcast Engineering

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Recently, human action recognition have been developed for various broadcasting and video process. Since a video can consist of various scenes, keypoint approaches have been more attracted than template based methods for real application. Keypoint approahces tried to find regions having motion in video, and made 3-dimensional patches. Then, descriptors using histograms were computed from the patches, and a classifier based on machine learning method was applied to detect actions in video. However, a single classifier was difficult to handle various human actions. In order to improve this problem, approaches using multi classifiers were used to detect and to recognize objects. Thus, we propose a new human action recognition using decision-level fusion with support vector machine and sparse representation. The proposed method extracted descriptors based on keypoint approach from a video, and acquired results from each classifier for human action recognition. Then, we applied weights which were acquired by training stage to fuse each results from two classifiers. The experiment results in this paper show better result than a previous fusion method.

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Trainable classifier-fusion schemes: An application to pedestrian detection

Cited by 122 publications

References 11 publications

An Automated System for Garment Texture Design Class Identification

An Automated System for Garment Texture Design Class Identification

Comparative study between deep learning and bag of visual words for wild-animal recognition

A Study for Improved Human Action Recognition using Multi-classifiers

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