Visual descriptors for scene categorization: experimental evaluation

Xue, Wenping; Phung, Son Lam; Bouzerdoum, A.

doi:10.1007/s10462-015-9448-4

Cited by 17 publications

(14 citation statements)

References 143 publications

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“…Local feature descriptors are often used in vision-based object recognition [11,21], retrieval [6], or scene categorisation [38]. However, there is still a place for faster and more robust techniques, able to successfully describe and match images despite various transformations, distortions, or illumination conditions [6,12,24,25].…”

Section: Proposed Methodsmentioning

confidence: 99%

An approach to vision-based localisation with binary features for partially sighted people

Oszust

Padjasek

Kasprzyk

2017

SIViP

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In this paper, an approach to the development of a localisation system for supporting visually impaired people is proposed. Instead of using unique visual markers or radio tags, this approach relies on image recognition with local feature descriptors. In order to provide fast and robust keypoint description, a new binary descriptor is introduced. The descriptor computation pipeline selects four image patches with scale-dependent sizes around the keypoint and then places five square pixel blocks within each patch. The binary string is obtained in pairwise tests between directional gradients obtained for blocks. In contrary to other binary descriptors, tests take into account gradient values obtained for blocks from all patches. The proposed approach is extensively tested using six demanding image datasets. Some of them contain labelled indoor and outdoor images under different real-world transformations, as well as challenging illumination conditions. Two datasets were prepared for the needs of this research. Experimental evaluation reveals that the introduced binary descriptor is more robust and achieves shorter computation time than state-of-the-art floating-point and binary descriptors. Furthermore, the approach outperforms other techniques in image recognition tasks, making it more suitable for the vision-based localisation.

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Section: Proposed Methodsmentioning

confidence: 99%

An approach to vision-based localisation with binary features for partially sighted people

Oszust

Padjasek

Kasprzyk

2017

SIViP

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“…For finding various features like color, shape, and histogram orientation of edge, different approaches and descriptors like SIFT, HOG, GIST, CNN and SEV were applied by researchers [40]. Color and shape of the signs are totally different from the natural environment.…”

Section: Proposed Descriptormentioning

confidence: 99%

A novel fusion mixture of active experts algorithm for traffic signs recognition

Ahangi

Langroudi

Yazdanpanah

et al. 2019

Multimed Tools Appl

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Traffic sign recognition is an important problem in today's applications. In this paper, by combining ensemble and active learning methods, a novel fusion mixture of active experts algorithm is proposed for this problem. The active learning algorithm is a popular method for reducing the number of samples. The primary goal of active learning is diminishing complexity, increasing the convergence rate, speeding up training process, and decreasing the cost of samples labeling. The active learning, hence, chooses informative samples to train. In addition, ensemble methods are a combination of simple classifiers for improving accuracy. Each classifier tries to learn a region of dataset better than other regions that all opinions are considered on ensemble methods as an ultimate decision. The mixture of experts is one of the most modern hybrid methods in which the training process takes a relatively long time, and it is a problem for large datasets. Our proposed Mixture of Active Experts tries to solve this problem. It decreases the training time process and increases the speed of convergence for finding optimal weights by selecting only informative samples in active learning phase. It is also applicable for online situations, in which the model should be trained continuously. The results of different experiments on German Traffic Sign Recognition Benchmark dataset demonstrate that the proposed method shows 96.69% accuracy and achieved the 6th rank among all the state of the art algorithms using smaller number (only 60%) of training samples.

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“…Spatial pyramid image representation, achieved by computing histograms of SIFT descriptors [18] extracted from image sub-regions, has been proposed. In a separate study, several state-of-the-art visual descriptors [19] were evaluated, using classification accuracy ratings for four benchmark scene data sets, such as an eight-outdoor-scene data set, a 15-scene data set, a 67-indoor-scene data set, and the SUN397 data set. The major contribution of this study was the integration of proposed Ohta Color-GIST wavelet descriptors and CENTRIST (spatial pyramid representation) descriptors, to recognize complex indoor versus outdoor scenes for MAV navigation in GPS-denied indoor and outdoor environments.…”

Section: Introductionmentioning

confidence: 99%

Indoor versus outdoor scene recognition for navigation of a micro aerial vehicle using spatial color gist wavelet descriptors

Ganesan

Anbarasu

2019

Vis. Comput. Ind. Biomed. Art

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In the context of improved navigation for micro aerial vehicles, a new scene recognition visual descriptor, called spatial color gist wavelet descriptor (SCGWD), is proposed. SCGWD was developed by combining proposed Ohta color-GIST wavelet descriptors with census transform histogram (CENTRIST) spatial pyramid representation descriptors for categorizing indoor versus outdoor scenes. A binary and multiclass support vector machine (SVM) classifier with linear and non-linear kernels was used to classify indoor versus outdoor scenes and indoor scenes, respectively. In this paper, we have also discussed the feature extraction methodology of several, state-of-the-art visual descriptors, and four proposed visual descriptors (Ohta color-GIST descriptors, Ohta color-GIST wavelet descriptors, enhanced Ohta color histogram descriptors, and SCGWDs), in terms of experimental perspectives. The proposed enhanced Ohta color histogram descriptors, Ohta color-GIST descriptors, Ohta color-GIST wavelet descriptors, SCGWD, and state-of-the-art visual descriptors were evaluated, using the Indian Institute of Technology Madras Scene Classification Image Database two, an Indoor-Outdoor Dataset, and the Massachusetts Institute of Technology indoor scene classification dataset [(MIT)-67]. Experimental results showed that the indoor versus outdoor scene recognition algorithm, employing SVM with SCGWDs, produced the highest classification rates (CRs)-95.48% and 99.82% using radial basis function kernel (RBF) kernel and 95.29% and 99.45% using linear kernel for the IITM SCID2 and Indoor-Outdoor datasets, respectively. The lowest CRs-2.08% and 4.92%, respectivelywere obtained when RBF and linear kernels were used with the MIT-67 dataset. In addition, higher CRs, precision, recall, and area under the receiver operating characteristic curve values were obtained for the proposed SCGWDs, in comparison with state-of-the-art visual descriptors.

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Visual descriptors for scene categorization: experimental evaluation

Cited by 17 publications

References 143 publications

An approach to vision-based localisation with binary features for partially sighted people

An approach to vision-based localisation with binary features for partially sighted people

A novel fusion mixture of active experts algorithm for traffic signs recognition

Indoor versus outdoor scene recognition for navigation of a micro aerial vehicle using spatial color gist wavelet descriptors

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