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Results 1 - 10
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689
Attribute and Simile Classifiers for Face Verification
- In IEEE International Conference on Computer Vision (ICCV
, 2009
"... We present two novel methods for face verification. Our first method – “attribute ” classifiers – uses binary classifiers trained to recognize the presence or absence of describable aspects of visual appearance (e.g., gender, race, and age). Our second method – “simile ” classifiers – removes the ma ..."
Abstract
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Cited by 325 (14 self)
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the manual labeling required for attribute classification and instead learns the similarity of faces, or regions of faces, to specific reference people. Neither method requires costly, often brittle, alignment between image pairs; yet, both methods produce compact visual descriptions, and work on real
Age Classification from Facial Images
- In Proc. IEEE Conf. Computer Vision and Pattern Recognition
, 1999
"... This paper presents a theory and practical computations for visual age classification from facial images. Currently, the theory has only beenimplemented to classify input images into one of three agegroups: babies, young adults, and senior adults. The computations are based on cranio-facial developm ..."
Abstract
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Cited by 89 (1 self)
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This paper presents a theory and practical computations for visual age classification from facial images. Currently, the theory has only beenimplemented to classify input images into one of three agegroups: babies, young adults, and senior adults. The computations are based on cranio
A Neura-Statistical Approach to Multitemporal and Multisource Remote-Sensing Image Classification
- IEEE Transactions on Geoscience and Remote Sensing
, 1999
"... Abstract—A data fusion approach to the classification of multi-source and multitemporal remote-sensing images is proposed. The method is based on the application of the Bayes rule for minimum error to the “compound ” classification of pairs of multisource images acquired at two different dates. In p ..."
Abstract
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Cited by 33 (7 self)
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approach. Index Terms — Data fusion, expectation maximization, im-age classification, multisource multitemporal images, neural networks, remote sensing. I.
Image classification based on Markov random
"... This paper considers image classification based on a Markov random field (MRF), where the random field proposed here adopts Jeffreys divergence between category-specific probability densities. The classification method based on the proposed MRF is shown to be an extension of Switzer’s smoothing meth ..."
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method, which is applied in re-mote sensing and geospatial communities. Furthermore, the exact error rates due to the proposed and Switzer’s methods are obtained under the simple setup, and several properties are derived. Our method is applied to a benchmark data set of im-age classification
Image Classification Based on Complex Wavelet Structural Similarity
"... Complex wavelet structural similarity (CW-SSIM) index has been recognized as a novel image similarity measure of broad potential applications due to its robustness to small geometric distortions such as translation, scaling and rotation of images. Nevertheless, how to make the best use of it in im-a ..."
Abstract
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Cited by 1 (0 self)
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Complex wavelet structural similarity (CW-SSIM) index has been recognized as a novel image similarity measure of broad potential applications due to its robustness to small geometric distortions such as translation, scaling and rotation of images. Nevertheless, how to make the best use of it in im-age
FAST CORTICAL KEYPOINTS FOR REAL-TIME OBJECT RECOGNITION
"... Best-performing object recognition algorithms employ a large number features extracted on a dense grid, so they are too slow for real-time and active vision. In this paper we present a fast cortical keypoint detector for extracting meaningful points from images. It is competitive with state-of-the-a ..."
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-of-the-art detectors and particularly well-suited for tasks such as ob-ject recognition. We show that by using these points we can achieve state-of-the-art categorization results in a fraction of the time required by competing algorithms. Index Terms — Computer vision, Object recognition, Im-age classification, Gabor
Learning relevant image features with multiple-kernel classifications
- IEEE Trans. Geosci. Remote Sens
, 2010
"... Abstract—The increase in spatial and spectral resolution of the satellite sensors, along with the shortening of the time-revisiting periods, has provided high-quality data for remote sensing im-age classification. However, the high-dimensional feature space induced by using many heterogeneous inform ..."
Abstract
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Cited by 20 (4 self)
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Abstract—The increase in spatial and spectral resolution of the satellite sensors, along with the shortening of the time-revisiting periods, has provided high-quality data for remote sensing im-age classification. However, the high-dimensional feature space induced by using many heterogeneous
Deeply coupled auto-encoder networks for cross-view classification. arXiv preprint arXiv:1402.2031
, 2014
"... The comparison of heterogeneous samples extensively exists in many applications, especially in the task of im-age classification. In this paper, we propose a simple but effective coupled neural network, called Deeply Coupled Autoencoder Networks (DCAN), which seeks to build two deep neural networks, ..."
Abstract
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Cited by 2 (1 self)
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The comparison of heterogeneous samples extensively exists in many applications, especially in the task of im-age classification. In this paper, we propose a simple but effective coupled neural network, called Deeply Coupled Autoencoder Networks (DCAN), which seeks to build two deep neural networks
1Feature Extraction for Hyperspectral Imagery via Ensemble Localized Manifold Learning
"... Abstract—A feature extraction approach for hyperspctral im-age classification has been developed. Multiple linear manifolds are learned to characterize the original data based on their locations in the feature space, and an ensemble of classifier is then trained using all these manifolds. Such manif ..."
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Abstract—A feature extraction approach for hyperspctral im-age classification has been developed. Multiple linear manifolds are learned to characterize the original data based on their locations in the feature space, and an ensemble of classifier is then trained using all these manifolds
Multi-Scale Orderless Pooling of Deep Convolutional Activation Features
"... Abstract. Deep convolutional neural networks (CNN) have shown their promise as a universal representation for recognition. However, global CNN activations lack geometric invariance, which limits their robustness for classification and matching of highly variable scenes. To improve the invariance of ..."
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Cited by 32 (2 self)
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level separately, and concatenates the result. The resulting MOP-CNN representation can be used as a generic feature for either supervised or unsupervised recognition tasks, from im-age classification to instance-level retrieval; it consistently outperforms global CNN activations without requiring any
Results 1 - 10
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689
