OpenRaiser/Intern-Atlas huggingface dataset:
An algebraic model for fast corner detection
Authors: Andrew R. Willis, Yunfeng Sui
Venue: ICCV
Year: 2009

This paper revisits the classical problem of detecting interest points, popularly known as “corners,” in 2D images by proposing a technique based on fitting algebraic shape models to contours in the edge image. Our method for corner detection is targeted for use on structural images, i.e., images that contain man-made structures for which corner detection algorithms are known to perform well. Further, our detector seeks to find image regions that contain two distinct linear contours that intersect. We define the intersection point as the corner, and, in contrast to previous approaches such as the Harris detector, we consider the spatial coherence of the edge points, i.e., the fact that the edge points must lie close to one of the two intersecting lines, an important aspect to stable corner detection. Comparisons between results for the proposed method and that for several popular feature detectors are provided using input images exhibiting a number of standard image variations, including blurring, affine transformation, scaling, rotation, and illumination variation. A modified version of the repeatability rate is proposed for evaluating the stability of the detector under these variations which requires a 1-to-1 mapping between matched features. Using this performance metric, our method is found to perform well in contrast to several current methods for corner detection. Discussion is provided that motivates our method of evaluation and provides an explanation for the observed performance of our algorithm in contrast to other algorithms. Our approach is distinct from other contour-based methods since we need only compute the edge image, from which we explicitly solve for the unknown linear contours and their intersections that provide image corner location estimates. The key benefits to this approach are: (1) performance (in space and time); since no image pyramid (space) and no edge-linking (time) is required and (2) compactness; the estimated model includes the corner location, and direction of the incoming contours in space, i.e., a complete model of the local corner geometry.
Beyond connecting the dots: A polynomial-time algorithm for segmentation and boundary estimation with imprecise user input
Authors: Thomas Windheuser, Thomas Schoenemann, Daniel Cremers
Venue: ICCV
Year: 2009

We propose a polynomial-time algorithm for segmentation and (open) boundary estimation which takes into account a series of user-specified attraction points. In contrast to existing algorithms which impose that the segmenting boundary passes through these points, our algorithm allows an imprecision in the user input. An energy minimization approach imposes that the segmenting boundary optimally passes along high-contrast edges in such a way that at least one point along the computed boundary is as close as possible to any given attraction point. In this sense, the user input can be seen as a soft constraint. We prove that the resulting optimization problem is NP-hard. We prove that in the case that the user attraction points are ordered, then optimal solutions can be computed in polynomial time using a shortest path formulation in an appropriately constructed four-dimensional graph spanned by the image pixels, a set of tangent angles and the user attraction points. Experimental results on a variety of images demonstrate that good quality segmentations can be obtained with a few imprecise user clicks.
A global perspective on MAP inference for low-level vision
Authors: Oliver J. Woodford, Carsten Rother, Vladimir Kolmogorov
Venue: ICCV
Year: 2009

In recent years the Markov Random Field (MRF) has become the de facto probabilistic model for low-level vision applications. However, in a maximum a posteriori (MAP) framework, MRFs inherently encourage delta function marginal statistics. By contrast, many low-level vision problems have heavy tailed marginal statistics, making the MRF model unsuitable. In this paper we introduce a more general Marginal Probability Field (MPF), of which the MRF is a special, linear case, and show that convex energy MPFs can be used to encourage arbitrary marginal statistics. We introduce a flexible, extensible framework for effectively optimizing the resulting NP-hard MAP problem, based around dual-decomposition and a modified min-cost flow algorithm, and which achieves global optimality in some instances. We use a range of applications, including image denoising and texture synthesis, to demonstrate the benefits of this class of MPF over MRFs.
Resilient Subclass Discriminant Analysis
Authors: Dijia Wu, Kim L. Boyer
Venue: ICCV
Year: 2009

We propose a dimension reduction technique named Resilient Subclass Discriminant Analysis (RSDA) for high dimensional classification problems. The technique iteratively estimates the subclass division by embedding the Fisher Discriminant Analysis (FDA) with Expectation-Maximization (EM) in Gaussian Mixture Models (GMM). The new method maintains the adaptability of SDA to a wide range of data distributions by approximating the distribution of each class as a mixture of Gaussians, and provides superior feature selection performance to SDA with modified EM clustering that estimates a posteriori probability of latent variables in lower-dimensional Fisher's discriminant space, which also improves the robustness in problems of small training datasets compared with conventional EM algorithm. Extensive experiments and comparison results against other well-known Discriminant Analysis (DA) methods are presented using synthetic data, benchmark datasets as well as a real computational vision problem.
Real-time visual tracking via Incremental Covariance Tensor Learning
Authors: Yi Wu 0001, Jian Cheng 0001, Jinqiao Wang, Hanqing Lu
Venue: ICCV
Year: 2009

Visual tracking is a challenging problem, as an object may change its appearance due to pose variations, illumination changes, and occlusions. Many algorithms have been proposed to update the target model using the large volume of available information during tracking, but at the cost of high computational complexity. To address this problem, we present a tracking approach that incrementally learns a low-dimensional covariance tensor representation, efficiently adapting online to appearance changes for each mode of the target with only ̃(1) computational complexity. Moreover, a weighting scheme is adopted to ensure less modeling power is expended fitting older observations. Both of these features contribute measurably to improving overall tracking performance. Tracking is then led by the Bayesian inference framework in which a particle filter is used to propagate sample distributions over time. With the help of integral images, our tracker achieves real-time performance. Extensive experiments demonstrate the effectiveness of the proposed tracking algorithm for the targets undergoing appearance variations.
A multi-sample, multi-tree approach to bag-of-words image representation for image retrieval
Authors: Zhong Wu, Qifa Ke, Jian Sun 0001, Heung-Yeung Shum
Venue: ICCV
Year: 2009

The state-of-the-art content based image retrieval systems has been significantly advanced by the introduction of SIFT features and the bag-of-words image representation. Converting an image into a bag-of-words, however, involves three non-trivial steps: feature detection, feature description, and feature quantization. At each of these steps, there is a significant amount of information lost, and the resulted visual words are often not discriminative enough for large scale image retrieval applications. In this paper, we propose a novel multi-sample multi-tree approach to computing the visual word codebook. By encoding more information of the original image feature, our approach generates a much more discriminative visual word codebook that is also efficient in terms of both computation and space consumption, without losing the original repeatability of the visual features. We evaluate our approach using both a ground-truth data set and a real-world large scale image database. Our results show that a significant improvement in both precision and recall can be achieved by using the codebook derived from our approach.
Incremental discriminative-analysis of canonical correlations for action recognition
Authors: Xinxiao Wu, Wei Liang 0008, Yunde Jia
Venue: ICCV
Year: 2009

Human action recognition is a challenging problem due to the large changes of human appearance in the cases of partial occlusions, non-rigid deformations and high irregularities. It is difficult to collect a large set of training samples with the hope of covering all possible variations of an action. In this paper, we propose an online recognition method, namely Incremental Discriminant-Analysis of Canonical Correlations (IDCC), whose discriminative model is incrementally updated to capture the changes of human appearance and thereby facilitates the recognition task in changing environments. As the training sets are acquired sequentially instead of being given completely in advance, our method is able to compute a new discriminant matrix by updating the existing one using the eigenspace merging algorithm. Experimental results on both Weizmann and KTH action data sets show that our method performs better than state-of-the-art methods on both accuracy and efficiency. Moreover, the robustness of our method is demonstrated on the irregular action recognition.
Multimodal partial estimates fusion
Authors: Jiang Xu 0002, Junsong Yuan 0001, Ying Wu 0001
Venue: ICCV
Year: 2009

Fusing partial estimates is a critical and common problem in many computer vision tasks such as part-based detection and tracking. It generally becomes complicated and intractable when there are a large number of multimodal partial estimates, and thus it is desirable to find an effective and scalable fusion method to integrate these partial estimates. This paper presents a novel and effective approach to fusing multimodal partial estimates in a principled way. In this new approach, fusion is related to a computational geometry problem of finding the minimum-volume orthotope, and an effective and scalable branch and bound search algorithm is designed to obtain the global optimal solution. Experiments on tracking articulated objects and occluded objects show the effectiveness of the proposed approach.
Group-sensitive multiple kernel learning for object categorization
Authors: Jingjing Yang, Yuanning Li, Yonghong Tian 0001, Lingyu Duan, Wen Gao 0001
Venue: ICCV
Year: 2009

In this paper, we propose a group-sensitive multiple kernel learning (GS-MKL) method to accommodate the intra-class diversity and the inter-class correlation for object categorization. By introducing an intermediate representation “group” between images and object categories, GS-MKL attempts to find appropriate kernel combination for each group to get a finer depiction of object categories. For each category, images within a group share a set of kernel weights while images from different groups may employ distinct sets of kernel weights. In GS-MKL, such group-sensitive kernel combinations together with the multi-kernels based classifier are optimized in a joint manner to seek a trade-off between capturing the diversity and keeping the invariance for each category. Extensive experiments show that our proposed GS-MKL method has achieved encouraging performance over three challenging datasets.
Detection driven adaptive multi-cue integration for multiple human tracking
Authors: Ming Yang 0007, Fengjun Lv, Wei Xu 0007, Yihong Gong
Venue: ICCV
Year: 2009

In video surveillance scenarios, appearances of both human and their nearby scenes may experience large variations due to scale and view angle changes, partial occlusions, or interactions of a crowd. These challenges may weaken the effectiveness of a dedicated target observation model even based on multiple cues, which demands for an agile framework to adjust target observation models dynamically to maintain their discriminative power. Towards this end, we propose a new adaptive way to integrate multi-cue in tracking multiple human driven by human detections. Given a human detection can be reliably associated with an existing trajectory, we adapt the way how to combine specifically devised models based on different cues in this tracker so as to enhance the discriminative power of the integrated observation model in its local neighborhood. This is achieved by solving a regression problem efficiently. Specifically, we employ 3 observation models for a single person tracker based on color models of part of torso regions, an elliptical head model, and bags of local features, respectively. Extensive experiments on 3 challenging surveillance datasets demonstrate long-term reliable tracking performance of this method.
Deformation invariant image matching by spectrally controlled diffeomorphic alignment
Authors: Christopher M. Yang, Sai Ravela
Venue: ICCV
Year: 2009

We present a new approach to deformation invariant image matching. Our matcher (a) aligns templates to targets over a broad range of nonlinear deformations, (b) factors the total deformation into spectral categories, where low wavenumber deformations are smooth and global and high wavenumbers are turbulent and local, and (c) weighs the reduction in template-target misfit within each category to differentiate between relevant and irrelevant deformations. It accomplishes this by aligning images in a scale-cascaded fashion, with more complex, local deformations following simpler, more global ones. Each step of the cascade involves finding an iterative solution to a nonlinear optimization problem using a Gabor deformation basis. Cascaded alignment makes deformation invariant matching feasible and efficient. Our approach is applied to recognize the flexible bodies of salamanders from a large database; results indicate that the method is very promising.
A biased sampling strategy for object categorization
Authors: Lei Yang 0063, Nanning Zheng 0001, Jie Yang 0001, Mei Chen, Hong Chen
Venue: ICCV
Year: 2009

In this paper, we present a biased sampling strategy for object class modeling, which can effectively circumvent the scene matching problem commonly encountered in statistical image-based object categorization. The method optimally combines the bottom-up, biologically inspired saliency information with loose, top-down class prior information to form a probabilistic distribution for feature sampling. When sampling over different positions and scales of patches, the weak spatial coherency is preserved by a segment-based analysis. We evaluate the proposed sampling strategy within the bag-of-features (BoF) object categorization framework on three public data sets. Our technique outperforms other state-of-the-art sampling technologies, and leads to a better performance in object categorization on VOC2008 dataset.
Learning deformable action templates from cluttered videos
Authors: Benjamin Z. Yao, Song Chun Zhu
Venue: ICCV
Year: 2009

In this paper, we present a Deformable Action Template (DAT) model that is learnable from cluttered real-world videos with weak supervisions. In our generative model, an action template is a sequence of image templates each of which consists of a set of shape and motion primitives (Gabor wavelets and optical-flow patches) at selected orientations and locations. These primitives are allowed to slightly perturb their locations and orientations to account for spatial deformations. We use a shared pursuit algorithm to automatically discover a best set of primitives and weights by maximizing the likelihood over one or more aligned training examples. Since it is extremely hard to accurately label human actions from real-world videos, we use a threestep semi-supervised learning procedure. 1) For each human action class, a template is initialized from a labeled (one bounding-box per frame) training video. 2) The template is used to detect actions from other training videos of the same class by a dynamic space-time warping algorithm, which searches a best match between the template and target video in 5D space (x, y, scale, ttemplate and ttarget) using dynamic programming. 3) The template is updated by the shared pursuit algorithm over all aligned videos. The 2nd and 3rd steps iterate several times to arrive at an optimal action template. We tested our algorithm on a cluttered action dataset (the CMU dataset) and achieved favorable performance than [7]. Our classification performance on the KTH dataset is also comparable to state-of-the-arts. 1.
Is a detector only good for detection?
Authors: Quan Yuan, Stan Sclaroff
Venue: ICCV
Year: 2009

A common design of an object recognition system has two steps, a detection step followed by a foreground within-class classification step. For example, consider face detection by a boosted cascade of detectors followed by face ID recognition via one-vs-all (OVA) classifiers. Another example is human detection followed by pose recognition. Although the detection step can be quite fast, the foreground within-class classification process can be slow and becomes a bottleneck. In this work, we formulate a filter-and-refine scheme, where the binary outputs of the weak classifiers in a boosted detector are used to identify a small number of candidate foreground state hypotheses quickly via Hamming distance or weighted Hamming distance. The approach is evaluated in three applications: face recognition on the FRGC V2 data set, hand shape detection and parameter estimation on a hand data set and vehicle detection and view angle estimation on a multi-view vehicle data set. On all data sets, our approach has comparable accuracy and is at least five times faster than the brute force approach.
Spectral error correcting output codes for efficient multiclass recognition
Authors: Xiao Zhang, Lin Liang, Heung-Yeung Shum
Venue: ICCV
Year: 2009

The error correcting output codes (ECOC) is a general framework to extend any binary classifier to the multiclass case. Finding the optimal ECOC is known as a NP hard problem. In this paper, we present a spectral analysis approach for the design of ECOC. We construct a similarity graph of the classes and generate ECOC with a subset of thresholded eigenvectors of the graph Laplacian. Using the spectral analysis, the coding efficiency, classifier's diversity, Hamming distance among codewords, and binary classifiers' accuracy can be simultaneously considered. The resulting ECOC is efficient, thus only a small set of binary classifiers are to be evaluated when making a decision. In experiments with large multiclass problems, our method is between 3 and 12 times faster comparing to one-against-all, with comparable classification accuracy. Our method also shows a better performance than the most of leading methods, e.g., ClassMap, random dense ECOC, random sparse ECOC, and discriminant ECOC.
Efficient human pose estimation via parsing a tree structure based human model
Authors: Xiaoqin Zhang 0002, Changcheng Li, Xiaofeng Tong, Weiming Hu 0004, Stephen J. Maybank, Yimin Zhang 0002
Venue: ICCV
Year: 2009

Human pose estimation is the task of determining the states (location, orientation and scale) of each body part. It is important for many vision understanding applications, e.g. visual interactive gaming, immersive virtual reality, content-based image retrieval, etc. However, it remains a challenging task because of unknown image background, presence of clutter, partial occlusion and especially the high dimensional state space (usually 30+ dimensions). In this paper, we contribute to human pose estimation in two aspects. First, we design two efficient Markov Chain dynamics under the data-driven Markov Chain Monte Carlo (DDMCMC) framework to effectively explore the complex solution space. Second, we parse the tree structure state space into a lexicographic order according to the image observations and body topology, and the optimization process is conducted in this order. This realizes a much more efficient exploration than the sampling based search and exhaustive search, and thus achieves a tremendous speed-up. Experimental results demonstrate the efficiency and effectiveness of the proposed method in estimating various kinds of human poses, even with cluttered background , poor illumination or partial self-occlusion.
Efficient indexing for large scale visual search
Authors: Xiao Zhang, Zhiwei Li 0006, Lei Zhang 0001, Wei-Ying Ma, Heung-Yeung Shum
Venue: ICCV
Year: 2009

With the popularity of “bag of visual terms” representations of images, many text indexing techniques have been applied in large-scale image retrieval systems. However, due to a fundamental difference between an image query (e.g. 1500 visual terms) and a text query (e.g. 3-5 terms), the usages of some text indexing techniques, e.g. inverted list, are misleading. In this work, we develop a novel indexing technique for this problem. The basic idea is to decompose a document-like representation of an image into two components, one for dimension reduction and the other for residual information preservation. The computing of similarity of two images can be transferred to measuring similarities of their components. The decomposition has two major merits: (1) these components have good properties which enable them to be efficiently indexed and retrieved; (2) The decomposition has better generalization ability than other dimension reduction algorithms. The decomposition can be achieved by either a graphical model or a matrix factorization approach. Theoretic analysis and extensive experiments over a 2.3 million image database show that this framework is scalable to index large scale image database to support fast and accurate visual search.
Finding shareable informative patterns and optimal coding matrix for multiclass boosting
Authors: Bang Zhang, Getian Ye, Yang Wang 0002, Jie Xu 0008, Gunawan Herman
Venue: ICCV
Year: 2009

A multiclass classification problem can be reduced to a collection of binary problems using an error-correcting coding matrix that specifies the binary partitions of the classes. The final classifier is an ensemble of base classifiers learned on binary problems and its performance is affected by two major factors: the qualities of the base classifiers and the coding matrix. Previous studies either focus on one of these factors or consider two factors separately. In this paper, we propose a new multiclass boosting algorithm called AdaBoost.SIP that considers both two factors simultaneously. In this algorithm, informative patterns, which are shareable by different classes rather than only discriminative on specific single class, are generated at first. Then the binary partition preferred by each pattern is found by performing stage-wise functional gradient descent on a margin-based cost function. Finally, base classifiers and coding matrix are optimized simultaneously by maximizing the negative gradient of such cost function. The proposed algorithm is applied to scene and event recognition and experimental results show its effectiveness in multiclass classification.
Quantifying contextual information for object detection
Authors: Wei-Shi Zheng 0001, Shaogang Gong, Tao Xiang 0002
Venue: ICCV
Year: 2009

Context is critical for minimising ambiguity in object detection. In this work, a novel context modelling framework is proposed without the need of any prior scene segmentation or context annotation. This is achieved by exploring a new polar geometric histogram descriptor for context representation. In order to quantify context, we formulate a new context risk function and a maximum margin context (MMC) model to solve the minimization problem of the risk function. Crucially, the usefulness and goodness of contextual information is evaluated directly and explicitly through a discriminant context inference method and a context confidence function, so that only reliable contextual information that is relevant to object detection is utilised. Experiments on PASCAL VOC2005 and i-LIDS datasets demonstrate that the proposed context modelling approach improves object detection significantly and outperforms a state-of-the-art alternative context model.