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27
HOGgles: Visualizing Object Detection Features ∗
"... We introduce algorithms to visualize feature spaces used by object detectors. The tools in this paper allow a human to put on ‘HOG goggles ’ and perceive the visual world as a HOG based object detector sees it. We found that these visualizations allow us to analyze object detection systems in new wa ..."
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We introduce algorithms to visualize feature spaces used by object detectors. The tools in this paper allow a human to put on ‘HOG goggles ’ and perceive the visual world as a HOG based object detector sees it. We found that these visualizations allow us to analyze object detection systems in new ways and gain new insight into the detector’s failures. For example, when we visualize the features for high scoring false alarms, we discovered that, although they are clearly wrong in image space, they do look deceptively similar to true positives in feature space. This result suggests that many of these false alarms are caused by our choice of feature space, and indicates that creating a better learning algorithm or building bigger datasets is unlikely to correct these errors. By visualizing feature spaces, we can gain a more intuitive understanding of our detection systems. Figure 1: An image from PASCAL and a high scoring car detection from DPM [8]. Why did the detector fail? 1.
Parzen Discriminant Analysis
"... In this paper, we propose a non-parametric Discriminant Analysis method (no assumption on the distributions of classes), called Parzen Discriminant Analysis (PDA). Through a deep investigation on the non-parametric density estimation, we find that minimizing/maximizing the distances between each dat ..."
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In this paper, we propose a non-parametric Discriminant Analysis method (no assumption on the distributions of classes), called Parzen Discriminant Analysis (PDA). Through a deep investigation on the non-parametric density estimation, we find that minimizing/maximizing the distances between each data sample and its nearby similar/dissimilar samples is equivalent to minimizing an upper bound of the Bayesian error rate. Based on this theoretical analysis, we define our criterion as maximizing the average local dissimilarity scatter with respect to a fixed average local similarity scatter. All local scatters are calculated in fixed size local regions, resembling the idea of Parzen estimation. Experiments in UCI machine learning database show that our method impressively outperforms other related neighbor based non-parametric methods. 1.
R.: Beta process joint dictionary learning for coupled feature spaces with application to single image super-resolution
- In: CVPR (2013
"... This paper addresses the problem of learning over-complete dictionaries for the coupled feature spaces, where the learned dictionaries also reflect the relationship be-tween the two spaces. A Bayesian method using a beta pro-cess prior is applied to learn the over-complete dictionar-ies. Compared to ..."
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This paper addresses the problem of learning over-complete dictionaries for the coupled feature spaces, where the learned dictionaries also reflect the relationship be-tween the two spaces. A Bayesian method using a beta pro-cess prior is applied to learn the over-complete dictionar-ies. Compared to previous couple feature spaces dictionary learning algorithms, our algorithm not only provides dic-tionaries that customized to each feature space, but also adds more consistent and accurate mapping between the two feature spaces. This is due to the unique property of the beta process model that the sparse representation can be decomposed to values and dictionary atom indicators. The proposed algorithm is able to learn sparse representations that correspond to the same dictionary atoms with the same sparsity but different values in coupled feature spaces, thus bringing consistent and accurate mapping between coupled feature spaces. Another advantage of the proposed method is that the number of dictionary atoms and their relative im-portance may be inferred non-parametrically. We compare the proposed approach to several state-of-the-art dictionary learning methods by applying this method to single image super-resolution. The experimental results show that dic-tionaries learned by our method produces the best super-resolution results compared to other state-of-the-art meth-ods. 1.
Inverting and visualizing features for object detection. arXiv
, 2012
"... This paper presents methods to visualize feature spaces commonly used in object detection. The tools in this paper allow a human to put on “feature space glasses ” and see the visual world as a computer might see it. We found that these “glasses ” allow us to gain insight into the behavior of comput ..."
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This paper presents methods to visualize feature spaces commonly used in object detection. The tools in this paper allow a human to put on “feature space glasses ” and see the visual world as a computer might see it. We found that these “glasses ” allow us to gain insight into the behavior of computer vision systems. We show a variety of experiments with our visualizations, such as examining the linear separability of recognition in HOG space, generating high scoring “super objects ” for an object detector, and diagnosing false positives. We pose the visualization problem as one of feature inversion, i.e. recovering the natural image that generated a feature descriptor. We describe four algorithms to tackle this task, with different trade-offs in speed, accuracy, and scalability. Our most successful algorithm uses ideas from sparse coding to learn a pair of dictionaries that enable regression between HOG features and natural images, and can invert features at interactive rates. We believe these visualizations are useful tools to add to an object detector researcher’s toolbox, and code is available. 1.
1A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution
"... We address single image super-resolution using a statistical prediction model based on sparse representations of low and high resolution image patches. The suggested model allows us to avoid any invariance assumption, which is a common practice in sparsity-based approaches treating this task. Predic ..."
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We address single image super-resolution using a statistical prediction model based on sparse representations of low and high resolution image patches. The suggested model allows us to avoid any invariance assumption, which is a common practice in sparsity-based approaches treating this task. Prediction of high resolution patches is obtained via MMSE estimation and the resulting scheme has the useful interpretation of a feedforward neural network. To further enhance performance we suggest data clustering and cascading several levels of the basic algorithm. We suggest a training scheme for the resulting network and demonstrate the capabilities of our algorithm, showing its advantages over existing methods based on a low and high resolution dictionary pair, in terms of computational complexity, numerical criteria and visual appearance. The suggested approach offers a desirable compromise between low computational complexity and reconstruction quality, when comparing it with state-of-the-art methods for single image super-resolution. Index Terms Dictionary learning, feedforward neural networks, MMSE estimation, nonlinear prediction, single image super-resolution, sparse representations, statistical models, restricted Boltzmann machine, zooming deblurring I.
Y.C.F.: Coupled dictionary and feature space learning with applications to cross-domain image synthesis and recognition
- In: ICCV. (2013
"... Cross-domain image synthesis and recognition are typi-cally considered as two distinct tasks in the areas of com-puter vision and pattern recognition. Therefore, it is not clear whether approaches addressing one task can be eas-ily generalized or extended for solving the other. In this paper, we pro ..."
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Cross-domain image synthesis and recognition are typi-cally considered as two distinct tasks in the areas of com-puter vision and pattern recognition. Therefore, it is not clear whether approaches addressing one task can be eas-ily generalized or extended for solving the other. In this paper, we propose a unified model for coupled dictionary and feature space learning. The proposed learning model not only observes a common feature space for associating cross-domain image data for recognition purposes, the de-rived feature space is able to jointly update the dictionaries in each image domain for improved representation. This is why our method can be applied to both cross-domain image synthesis and recognition problems. Experiments on a vari-ety of synthesis and recognition tasks such as single image super-resolution, cross-view action recognition, and sketch-to-photo face recognition would verify the effectiveness of our proposed learning model. 1.
Accurate Blur Models vs. Image Priors in Single Image Super-Resolution
"... Over the past decade, single image Super-Resolution (SR) research has focused on developing sophisticated image priors, leading to significant advances. Estimating and incorporating the blur model, that relates the high-res and low-res images, has received much less attention, however. In particular ..."
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Over the past decade, single image Super-Resolution (SR) research has focused on developing sophisticated image priors, leading to significant advances. Estimating and incorporating the blur model, that relates the high-res and low-res images, has received much less attention, however. In particular, the reconstruction constraint, namely that the blurred and downsampled high-res output should approximately equal the low-res input image, has been either ignored or applied with default fixed blur models. In this work, we examine the relative importance of the image prior and the reconstruction constraint. First, we show that an accurate reconstruction constraint combined with a simple gradient regularization achieves SR results almost as good as those of state-of-the-art algorithms with sophisticated image priors. Second, we study both empirically and theoretically the sensitivity of SR algorithms to the blur model assumed in the reconstruction constraint. We find that an accurate blur model is more important than a sophisticated image prior. Finally, using real camera data, we demonstrate that the default blur models of various SR algorithms may differ from the camera blur, typically leading to oversmoothed results. Our findings highlight the importance of accurately estimating camera blur in reconstructing raw low- res images acquired by an actual camera. 1.
Reconstruction Based Finger-Knuckle-Print Verification With Score Level Adaptive Binary Fusion
"... Abstract — Recently, a new biometrics identifier, namely finger knuckle print (FKP), has been proposed for personal authentica-tion with very interesting results. One of the advantages of FKP verification lies in its user friendliness in data collection. However, the user flexibility in positioning ..."
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Abstract — Recently, a new biometrics identifier, namely finger knuckle print (FKP), has been proposed for personal authentica-tion with very interesting results. One of the advantages of FKP verification lies in its user friendliness in data collection. However, the user flexibility in positioning fingers also leads to a certain degree of pose variations in the collected query FKP images. The widely used Gabor filtering based competitive coding scheme is sensitive to such variations, resulting in many false rejections. We propose to alleviate this problem by reconstructing the query sample with a dictionary learned from the template samples in the gallery set. The reconstructed FKP image can reduce much the enlarged matching distance caused by finger pose variations; however, both the intra-class and inter-class distances will be reduced. We then propose a score level adaptive binary fusion rule to adaptively fuse the matching distances before and after reconstruction, aiming to reduce the false rejections without increasing much the false acceptances. Experimental results on the benchmark PolyU FKP database show that the proposed method significantly improves the FKP verification accuracy.
Visualizing Object Detection Features
, 2013
"... We introduce algorithms to visualize feature spaces used by object detectors. The tools in this paper allow a human to put on ‘HOG goggles ’ and perceive the visual world as a HOG based object detector sees it. We found that these visualizations allow us to analyze object detection systems in new wa ..."
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We introduce algorithms to visualize feature spaces used by object detectors. The tools in this paper allow a human to put on ‘HOG goggles ’ and perceive the visual world as a HOG based object detector sees it. We found that these visualizations allow us to analyze object detection systems in new ways and gain new insight into the detector’s failures. For example, when we visualize high scoring false alarms, we discovered that, although they are clearly wrong in image space, they do look deceptively similar to true positives in feature space. This result suggests that many of these false alarms are caused by our choice of feature space, and indicates that creating a better learning algorithm or building bigger datasets is unlikely to correct these errors. By visualizing feature spaces, we can gain a more intuitive understanding of our detection systems.
Single-Image Super-Resolution: A Benchmark
"... Abstract. Single-image super-resolution is of great importance for vi-sion applications, and numerous algorithms have been proposed in recent years. Despite the demonstrated success, these results are often gener-ated based on different assumptions using different datasets and met-rics. In this pape ..."
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Abstract. Single-image super-resolution is of great importance for vi-sion applications, and numerous algorithms have been proposed in recent years. Despite the demonstrated success, these results are often gener-ated based on different assumptions using different datasets and met-rics. In this paper, we present a systematic benchmark evaluation for state-of-the-art single-image super-resolution algorithms. In addition to quantitative evaluations based on conventional full-reference metrics, hu-man subject studies are carried out to evaluate image quality based on visual perception. The benchmark evaluations demonstrate the perfor-mance and limitations of state-of-the-art algorithms which sheds light on future research in single-image super-resolution.
