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Free-shape subwindow search for object localization

Free-shape subwindow search for object localization

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Zhiqi Zhang Yu Cao Dhaval Salvi Kenton Oliver Jarrell Waggoner Song Wang Department of Computer Science and Engineering University of South Carolina Columbia SC USA

Object localization in an image is usually handled by searching for an optimal subwindow that tightly covers the object of interest. However, the subwindows considered in previous work are limited to rectangles or other specified, simple shapes. With such specified shapes, no subwindow can cover the object of interest tightly. As a result, the desired subwindow around the object of interest may not be optimal in terms of the localization objective function, and cannot be detected by a subwindow search algorithm. In this paper, we propose a new graph-theoretic approach for object localization by searching for an optimal subwindow without pre-specifying its shape. Instead, we require the resulting subwindow to be well aligned with edge pixels that are detected from the image. This requirement is quantified and integrated into the localization objective function based on the widely-used bag of visual words technique. We show that the ratio-contour graph algorithm can be adapted to find the optimal free-shape subwindow in terms of the new localization objective function. In the experiment, we test the proposed approach on the PASCAL VOC 2006 and VOC 2007 databases for localizing several categories of animals. We find that its performance is better than the previous efficient subwindow search algorithm.

关键词： Object detection Shape Image edge detection Image recognition computer vision computer science Pixel Testing Visual databases Animals

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PrivacyCam: a Privacy Preserving Camera Using uCLinux on the Blackfin DSP

PrivacyCam: a Privacy Preserving Camera Using uCLinux on the...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Ankur Chattopadhyay T.E. Boult Vision and Security Technology (VAST) Laboratory University of Colorado Colorado Springs Colorado Springs CO USA

Considerable research work has been done in the area of surveillance and biometrics, where the goals have always been high performance, robustness in security and cost optimization. With the emergence of more intelligent and complex video surveillance mechanisms, the issue of "privacy invasion" has been looming large. Very little investment or effort has gone into looking after this issue in an efficient and cost-effective way. The process of PICO (privacy through invertible cryptographic obscuration) is a way of using cryptographic techniques and combining them with image processing and video surveillance to provide a practical solution to the critical issue of "privacy invasion". This paper presents the idea and example of a realtime embedded application of the PICO technique, using uCLinux on the tiny Blackfin DSP architecture, along with a small Omnivision camera. It demonstrates how the practical problem of "privacy invasion" can be successfully addressed through DSP hardware in terms of smallness in size and cost optimization. After review of previous applications of "privacy protection", and system components, we discuss the "embedded jpeg-space" detection of regions of interest and the real time application of encryption techniques to improve privacy while allowing general surveillance to continue. The resulting approach permits full access (violation of privacy) only by access to the private-key to recover the decryption key, thereby striking a fine trade-off among privacy, security, cost and space.

关键词： Privacy Cameras Digital signal processing Cryptography Security Cost function Video surveillance Biometrics Robustness Investments

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Contextual pooling in image classification

Contextual pooling in image classification

引用

11th Asian Conference on computer vision, ACCV 2012

作者： Wu, Zifeng Huang, Yongzhen Wang, Liang Tan, Tieniu National Lab. of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783642373305

The original bag-of-words (BoW) model in terms of image classification treats each local feature independently, and thus ignores the spatial relationships between a feature and its neighboring features, namely, the feature's context. However, our intuition and empirical studies tell the importance of such spatial information. Although the global spatial information can be captured with the spatial pyramid matching scheme, the subject of capturing local spatial relationships between features is still open. In this paper, we propose a new method to embed such local spatial (context) information into the BoW model. A vector reflecting context information is firstly extracted along with each feature, context patterns are then code-specifically trained, and thus the context information is elegantly embedded into the BoW model by contextual pooling according to different context patterns. Extensive experiments on the PASCAL VOC 2007 dataset show that our method greatly enhances the BoW model, and achieves the state-of-the-art performance. © 2013 Springer-Verlag.

关键词： Image classification

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A Graph Reduction Method for 2D Snake Problems

A Graph Reduction Method for 2D Snake Problems

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Conference on computer vision and pattern recognition (CVPR)

作者： Jianhua Yan Keqi Zhang Chengcui Zhang Shu-Ching Chen Giri Narasimhan School of Computing and Information Sciences Florida International University USA Department of Environmental Studies & International Hurricane Research Center Florida State University USA Department of Computer and Information Sciences University of Alabama Birmingham USA

Energy-minimizing active contour models (snakes) have been proposed for solving many computer vision problems such as object segmentation, surface reconstruction, and object tracking. Dynamic programming which allows natural enforcement of constraints is an effective method for computing the global minima of energy functions. However, this method is only limited to snake problems with one dimensional (ID) topology (i.e., a contour) and cannot handle problems with two-dimensional (2D) topology. In this paper, we have extended the dynamic programming method to address the snake problems with 2D topology using a novel graph reduction algorithm. Given a 2D snake with first order energy terms, a set of reduction operations are defined and used to simplify the graph of the 2D snake into one single vertex while retaining the minimal energy of the snake. The proposed algorithm has a polynomial-time complexity bound and the optimality of the solution for a reducible 2D snake is guaranteed. However, not all types of 2D snakes can be reduced into one single vertex using the proposed algorithm. The reduction of general planar snakes is an NP-complete problem. The proposed method has been applied to optimize 2D building topology extracted from airborne LIDAR data to examine the effectiveness of the algorithm. The results demonstrate that the proposed approach successfully found the global optima for over 98% of building topology in a polynomial time.

关键词： Topology Dynamic programming Polynomials Active contours computer vision Object segmentation Surface reconstruction NP-complete problem Optimization methods Data mining

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Fast and robust object segmentation with the Integral Linear Classifier

Fast and robust object segmentation with the Integral Linear...

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Conference on computer vision and pattern recognition (CVPR)

作者： David Aldavert Ramon Lopez de Mantaras Arnau Ramisa Ricardo Toledo Computer Vision Center Department Computer Science University Autònoma de Barcelona Spain Artificial Intelligence Research Institute IIIA-CSIC INRIA-Grenoble France Artificial Intelligence Research Institute IIIA-CSIC Universidad Autonoma de Barcelona Spain

We propose an efficient method, built on the popular Bag of Features approach, that obtains robust multiclass pixel-level object segmentation of an image in less than 500ms, with results comparable or better than most state of the art methods. We introduce the Integral Linear Classifier (ILC), that can readily obtain the classification score for any image sub-window with only 6 additions and 1 product by fusing the accumulation and classification steps in a single operation. In order to design a method as efficient as possible, our building blocks are carefully selected from the quickest in the state of the art. More precisely, we evaluate the performance of three popular local descriptors, that can be very efficiently computed using integral images, and two fast quantization methods: the Hierarchical K-Means, and the Extremely Randomized Forest. Finally, we explore the utility of adding spatial bins to the Bag of Features histograms and that of cascade classifiers to improve the obtained segmentation. Our method is compared to the state of the art in the difficult Graz-02 and PASCAL 2007 Segmentation Challenge datasets.

关键词： Robustness Object segmentation Histograms Pixel Image segmentation Feature extraction computer vision computer science Artificial intelligence Quantization

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Segmenting Motions of Different Types by Unsupervised Manifold Clustering

Segmenting Motions of Different Types by Unsupervised Manifo...

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Conference on computer vision and pattern recognition (CVPR)

作者： Alvina Goh Rene Vidal Center for Imaging Science Johns Hopkins University Baltimore MD USA

We propose a novel algorithm for segmenting multiple motions of different types from point correspondences in multiple affine or perspective views. Since point trajectories associated with different motions live in different manifolds, traditional approaches deal with only one manifold type: linear subspaces for affine views, and homographic, bilinear and trilinear varieties for two and three perspective views. As real motion sequences contain motions of different types, we cast motion segmentation as a problem of clustering manifolds of different types. Rather than explicitly modeling each manifold as a linear, bilinear or multilinear variety, we use nonlinear dimensionality reduction to learn a low-dimensional representation of the union of all manifolds. We show that for a union of separated manifolds, the LLE algorithm computes a matrix whose null space contains vectors giving the segmentation of the data. An analysis of the variance of these vectors allows us to distinguish them from other vectors in the null space. This leads to a new algorithm for clustering both linear and nonlinear manifolds. Although this algorithm is theoretically designed for separated manifolds, our experiments demonstrate its performance on real data where this assumption does not hold. We test our algorithm on the Hopkins 155 motion segmentation database and achieve an average classification error of 4.8%, which compares favorably against state-of-the art multiframe motion segmentation methods.

关键词： Clustering algorithms Motion segmentation computer vision Vectors Null space Analysis of variance Data analysis Functional analysis Algorithm design and analysis Testing

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Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks

Learning and Transferring Mid-Level Image Representations us...

引用

IEEE Conference on computer vision and pattern recognition

作者： Maxime Oquab Leon Bottou Ivan Laptev Josef Sivic INRIA MSR

ISBN: (纸本)9781479951192

Convolutional neural networks (CNN) have recently shown outstanding image classification performance in the large-scale visual recognition challenge (ILSVRC2012). The success of CNNs is attributed to their ability to learn rich mid-level image representations as opposed to hand-designed low-level features used in other image classification methods. Learning CNNs, however, amounts to estimating millions of parameters and requires a very large number of annotated image samples. This property currently prevents application of CNNs to problems with limited training data. In this work we show how image representations learned with CNNs on large-scale annotated datasets can be efficiently transferred to other visual recognition tasks with limited amount of training data. We design a method to reuse layers trained on the ImageNet dataset to compute mid-level image representation for images in the PASCAL VOC dataset. We show that despite differences in image statistics and tasks in the two datasets, the transferred representation leads to significantly improved results for object and action classification, outperforming the current state of the art on Pascal VOC 2007 and 2012 datasets. We also show promising results for object and action localization.

关键词： Training Training data Visualization Image recognition Image representation Neural networks computer vision

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Learning to Group Objects

Learning to Group Objects

引用

IEEE Conference on computer vision and pattern recognition

作者： Victoria Yanulevskaya Jasper Uijlings Nicu Sebe University of Trento

ISBN: (纸本)9781479951192

This paper presents a novel method to generate a hypothesis set of class-independent object regions. It has been shown that such object regions can be used to focus computer vision techniques on the parts of an image that matter most leading to significant improvements in both object localisation and semantic segmentation in recent years. Of course, the higher quality of class-independent object regions, the better subsequent computer vision algorithms can perform. In this paper we focus on generating higher quality object hypotheses. We start from an oversegmentation for which we propose to extract a wide variety of region-features. We group regions together in a hierarchical fashion, for which we train a Random Forest which predicts at each stage of the hierarchy the best possible merge. Hence unlike other approaches, we use relatively powerful features and classifiers at an early stage of the generation of likely object regions. Finally, we identify and combine stable regions in order to capture objects which consist of dissimilar parts. We show on the PASCAL 2007 and 2012 datasets that our method yields higher quality regions than competing approaches while it is at the same time more computationally efficient.

关键词： Image color analysis Image segmentation Histograms Feature extraction Merging Radio frequency computer vision

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Color attributes for object detection

Color attributes for object detection

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Conference on computer vision and pattern recognition (CVPR)

作者： Fahad Shahbaz Khan Rao Muhammad Anwer Joost van de Weijer Andrew D. Bagdanov Maria Vanrell Antonio M. Lopez Computer Vision Laboratory Linköping University Sweden Computer Vision Center CS Department Universitat Authnòma de Barcelona Spain Media Integration and Communication Center University of Florence Italy

State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification, leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape. In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-of-the-art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods.

关键词： Image color analysis Object detection Shape Feature extraction Histograms Computational modeling Lighting

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The Hierarchical Isometric Self-Organizing Map for Manifold Representation

The Hierarchical Isometric Self-Organizing Map for Manifold ...

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Conference on computer vision and pattern recognition (CVPR)

作者： Haiying Guan Matthew Turk Computer Science Department University of California Santa Barbara CA USA

We present an algorithm, Hierarchical ISOmetric Self-Organizing Map (H-ISOSOM), for a concise, organized manifold representation of complex, non-linear, large scale, high-dimensional input data in a low dimensional space. The main contribution of our algorithm is threefold. First, we modify the previous ISOSOM algorithm by a local linear interpolation (LLl) technique, which maps the data samples from low dimensional space back to high dimensional space and makes the complete mapping pseudo-invertible. The modified-ISOSOM (M-ISOSOM) follows the global geometric structure of the data, and also preserves local geometric relations to reduce the nonlinear mapping distortion and make the learning more accurate. Second, we propose the H-ISOSOM algorithm for the computational complexity problem of Isomap, SOM and LLI and the nonlinear complexity problem of the highly twisted manifold. H-ISOSOM learns an organized structure of a non-convex, large scale manifold and represents it by a set of hierarchical organized maps. The hierarchical structure follows a coarse-to-fine strategy. According to the coarse global structure, it "unfolds " the manifold at the coarse level and decomposes the sample data into small patches, then iteratively learns the nonlinearity of each patch in finer levels. The algorithm simultaneously reorganizes and clusters the data samples in a low dimensional space to obtain the concise representation. Third, we give quantitative comparisons of the proposed method with similar methods on standard data sets. Finally, we apply H-ISOSOM to the problem of appearance-based hand pose estimation. Encouraging experimental results validate the effectiveness and efficiency of H-ISOSOM.

关键词： Nonlinear distortion Clustering algorithms Large-scale systems Independent component analysis Computational complexity Iterative algorithms Humans computer vision Principal component analysis Kernel

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