OpenRaiser/Intern-Atlas huggingface dataset:
Weakly supervised object detector learning with model drift detection
Authors: Parthipan Siva, Tao Xiang 0002
Venue: ICCV
Year: 2011

A conventional approach to learning object detectors uses fully supervised learning techniques which assumes that a training image set with manual annotation of object bounding boxes are provided. The manual annotation of objects in large image sets is tedious and unreliable. Therefore, a weakly supervised learning approach is desirable, where the training set needs only binary labels regarding whether an image contains the target object class. In the weakly supervised approach a detector is used to iteratively annotate the training set and learn the object model. We present a novel weakly supervised learning framework for learning an object detector. Our framework incorporates a new initial annotation model to start the iterative learning of a detector and a model drift detection method that is able to detect and stop the iterative learning when the detector starts to drift away from the objects of interest. We demonstrate the effectiveness of our approach on the challenging PASCAL 2007 dataset.
Positive definite dictionary learning for region covariances
Authors: Ravishankar Sivalingam, Daniel Boley, Vassilios Morellas, Nikolaos Papanikolopoulos
Venue: ICCV
Year: 2011

Sparse models have proven to be extremely successful in image processing and computer vision, and most efforts have been focused on sparse representation of vectors. The success of sparse modeling and the popularity of region covariances have inspired the development of sparse coding approaches for positive definite matrices. While in earlier work [1], the dictionary was pre-determined, it is clearly advantageous to learn a concise dictionary adaptively from the data at hand. In this paper, we propose a novel approach for dictionary learning over positive definite matrices. The dictionary is learned by alternating minimization between the sparse coding and dictionary update stages, and two different atom update methods are described. The online versions of the dictionary update techniques are also outlined. Experimental results demonstrate that the proposed learning methods yield better dictionaries for positive definite sparse coding. The learned dictionaries are applied to texture and face data, leading to improved classification accuracy and strong detection performance, respectively.
Spatiotemporal oriented energies for spacetime stereo
Authors: Mikhail Sizintsev, Richard P. Wildes
Venue: ICCV
Year: 2011

This paper presents a novel approach to recovering temporally coherent estimates of 3D structure of a dynamic scene from a sequence of binocular stereo images. The approach is based on matching spatiotemporal orientation distributions between left and right temporal image streams, which encapsulates both local spatial and temporal structure for disparity estimation. By capturing spatial and temporal structure in this unified fashion, both sources of information combine to yield disparity estimates that are naturally temporal coherent, while helping to resolve matches that might be ambiguous when either source is considered alone. Further, by allowing subsets of the orientation measurements to support different disparity estimates, an approach to recovering multilayer disparity from spacetime stereo is realized. The approach has been implemented with real-time performance on commodity GPUs. Empirical evaluation shows that the approach yields qualitatively and quantitatively superior disparity estimates in comparison to various alternative approaches, including the ability to provide accurate multilayer estimates in the presence of (semi)transparent and specular surfaces.
Efficient learning of sparse, distributed, convolutional feature representations for object recognition
Authors: Kihyuk Sohn, Dae Yon Jung, Honglak Lee, Alfred O. Hero III
Venue: ICCV
Year: 2011

Informative image representations are important in achieving state-of-the-art performance in object recognition tasks. Among feature learning algorithms that are used to develop image representations, restricted Boltzmann machines (RBMs) have good expressive power and build effective representations. However, the difficulty of training RBMs has been a barrier to their wide use. To address this difficulty, we show the connections between mixture models and RBMs and present an efficient training method for RBMs that utilize these connections. To the best of our knowledge, this is the first work showing that RBMs can be trained with almost no hyperparameter tuning to provide classification performance similar to or significantly better than mixture models (e.g., Gaussian mixture models). Along with this efficient training, we evaluate the importance of convolutional training that can capture a larger spatial context with less redundancy, as compared to non-convolutional training. Overall, our method achieves state-of-the-art performance on both Caltech 101 / 256 datasets using a single type of feature.
Tight convex relaxations for vector-valued labeling problems
Authors: Evgeny Strekalovskiy, Bastian Goldlücke, Daniel Cremers
Venue: ICCV
Year: 2011

The multi-label problem is of fundamental importance to computer vision, yet finding global minima of the associated energies is very hard and usually impossible in practice. Recently, progress has been made using continuous formulations of the multi-label problem and solving a convex relaxation globally, thereby getting a solution with optimality bounds. In this work, we develop a novel framework for continuous convex relaxations, where the label space is a continuous product space. In this setting, we can combine the memory efficient product relaxation of [9] with the much tighter relaxation of [5], which leads to solutions closer to the global optimum. Furthermore, the new setting allows us to formulate more general continuous regularizers, which can be freely combined in the different label dimensions. We also improve upon the relaxation of the products in the data term of [9], which removes the need for artificial smoothing and allows the use of exact solvers.
Visual word disambiguation by semantic contexts
Authors: Yu Su 0009, Frédéric Jurie
Venue: ICCV
Year: 2011

This paper presents a novel schema to address the polysemy of visual words in the widely used bag-of-words model. As a visual word may have multiple meanings, we show it is possible to use semantic contexts to disambiguate these meanings and therefore improve the performance of bag-of-words model. On one hand, for an image, multiple context-specific bag-of-words histograms are constructed, each of which corresponds to a semantic context. Then these histograms are merged by selecting only the most discriminative context for each visual word, resulting in a compact image representation. On the other hand, an image is represented by the occurrence probabilities of semantic contexts. Finally, when classifying an image, two image representations are combined at decision level to utilize the complementary information embedded in them. Experiments on three challenging image databases (PASCAL VOC 2007, Scene-15 and MSRCv2) show that our method significantly outperforms state-of-the-art classification methods.
HEAT: Iterative relevance feedback with one million images
Authors: Nicolae Suditu, François Fleuret
Venue: ICCV
Year: 2011

It has been shown repeatedly that iterative relevance feedback is a very efficient solution for content-based image retrieval. However, no existing system scales gracefully to hundreds of thousands or millions of images. We present a new approach dubbed Hierarchical and Expandable Adaptive Trace (HEAT) to tackle this problem. Our approach modulates on-the-fly the resolution of the interactive search in different parts of the image collection, by relying on a hierarchical organization of the images computed off-line. Internally, the strategy is to maintain an accurate approximation of the probabilities of relevance of the individual images while fixing an upper bound on the re- quired computation. Our system is compared on the ImageNet database to the state-of-the-art approach it extends, by conducting user evaluations on a sub-collection of 33,000 images. Its scalability is then demonstrated by conducting similar evaluations on 1,000,000 images.
Variational stereo in dynamic illumination
Authors: Yohay Swirski, Yoav Y. Schechner, Tal Nir
Venue: ICCV
Year: 2011

Temporal irradiance variations are useful for finding dense stereo correspondences. These variations can be created artificially using structured light. They also occur naturally underwater. We introduce a variational optimization formulation for finding a dense stereo correspondence field. It is based on multi-frame optical flow, adapted to stereo. The formulation uses a sequence of stereo frames, and yields dense and robust results. The inherent aperture problem of optical flow is resolved using a temporal sequence of stereo frame-pairs. The results are achieved even without considering epi-polar geometry. The method has the ability to handle dynamic stereo underwater, in harsh conditions of flickering illumination. The method is demonstrated experimentally both outdoors and indoors.
Source constrained clustering
Authors: Ekaterina H. Taralova, Fernando De la Torre, Martial Hebert
Venue: ICCV
Year: 2011

We consider the problem of quantizing data generated from disparate sources, e.g. subjects performing actions with different styles, movies with particular genre bias, various conditions in which images of objects are taken, etc. These are scenarios where unsupervised clustering produces inadequate codebooks because algorithms like K-means tend to cluster samples based on data biases (e.g. cluster subjects), rather than cluster similar samples across sources (e.g. cluster actions). We propose a new quantization technique, Source Constrained Clustering (SCC), which extends the K-means algorithm by enforcing clusters to group samples from multiple sources. We evaluate the method in the context of activity recognition from videos in an unconstrained environment. Experiments on several tasks and features show that using source information improves classification performance.
Face recognition via local sparse coding
Authors: Ilias Theodorakopoulos, Ioannis Rigas, George Economou, Spiros Fotopoulos
Venue: ICCV
Year: 2011

In this paper the face recognition problem is addressed in a part-based sparse approach through the comparison of respective facial regions between different images. To this purpose, a sparse coding procedure is applied to non-overlapping patches derived from frontal-face images, in order to extract local facial information. An adequate measure is introduced, incorporating the resulted sparse representation along with the Hamming distance, in order to express pairwise similarities between faces. Finally, a simple Nearest Neighbor classifier is employed to determine the identity of each facial image. In addition, a new criterion is presented for the rejection of outliers. The emerged face recognition scheme is evaluated using publicly available facial image databases, and the results are compared with those of other well-established recognition methods.
Understanding scenes on many levels
Authors: Joseph Tighe, Svetlana Lazebnik
Venue: ICCV
Year: 2011

This paper presents a framework for image parsing with multiple label sets. For example, we may want to simultaneously label every image region according to its basic-level object category (car, building, road, tree, etc.), superordinate category (animal, vehicle, manmade object, natural object, etc.), geometric orientation (horizontal, vertical, etc.), and material (metal, glass, wood, etc.). Some object regions may also be given part names (a car can have wheels, doors, windshield, etc.). We compute co-occurrence statistics between different label types of the same region to capture relationships such as “roads are horizontal,” “cars are made of metal,” “cars have wheels” but “horses have legs,” and so on. By incorporating these constraints into a Markov Random Field inference framework and jointly solving for all the label sets, we are able to improve the classification accuracy for all the label sets at once, achieving a richer form of image understanding.
The NBNN kernel
Authors: Tinne Tuytelaars, Mario Fritz, Kate Saenko, Trevor Darrell
Venue: ICCV
Year: 2011

Naive Bayes Nearest Neighbor (NBNN) has recently been proposed as a powerful, non-parametric approach for object classification, that manages to achieve remarkably good results thanks to the avoidance of a vector quantization step and the use of image-to-class comparisons, yielding good generalization. In this paper, we introduce a kernelized version of NBNN. This way, we can learn the classifier in a discriminative setting. Moreover, it then becomes straightforward to combine it with other kernels. In particular, we show that our NBNN kernel is complementary to standard bag-of-features based kernels, focussing on local generalization as opposed to global image composition. By combining them, we achieve state-of-the-art results on Caltech101 and 15 Scenes datasets. As a side contribution, we also investigate how to speed up the NBNN computations.
Robust and efficient parametric face alignment
Authors: Georgios Tzimiropoulos, Stefanos Zafeiriou, Maja Pantic
Venue: ICCV
Year: 2011

We propose a correlation-based approach to parametric object alignment particularly suitable for face analysis applications which require efficiency and robustness against occlusions and illumination changes. Our algorithm registers two images by iteratively maximizing their correlation coefficient using gradient ascent. We compute this correlation coefficient from complex gradients which capture the orientation of image structures rather than pixel intensities. The maximization of this gradient correlation coefficient results in an algorithm which is as computationally efficient as ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> norm-based algorithms, can be extended within the inverse compositional framework (without the need for Hessian recomputation) and is robust to outliers. To the best of our knowledge, no other algorithm has been proposed so far having all three features. We show the robustness of our algorithm for the problem of face alignment in the presence of occlusions and non-uniform illumination changes. The code that reproduces the results of our paper can be found at http://ibug.doc.ic.ac.uk/resources.
Weakly supervised semantic segmentation with a multi-image model
Authors: Alexander Vezhnevets, Vittorio Ferrari, Joachim M. Buhmann
Venue: ICCV
Year: 2011

We propose a novel method for weakly supervised semantic segmentation. Training images are labeled only by the classes they contain, not by their location in the image. On test images instead, the method predicts a class label for every pixel. Our main innovation is a multi-image model (MIM) - a graphical model for recovering the pixel labels of the training images. The model connects superpixels from all training images in a data-driven fashion, based on their appearance similarity. For generalizing to new test images we integrate them into MIM using a learned multiple kernel metric, instead of learning conventional classifiers on the recovered pixel labels. We also introduce an “objectness” potential, that helps separating objects (e.g. car, dog, human) from background classes (e.g. grass, sky, road). In experiments on the MSRC 21 dataset and the LabelMe subset of [18], our technique outperforms previous weakly supervised methods and achieves accuracy comparable with fully supervised methods.
Efficient Orthogonal Matching Pursuit using sparse random projections for scene and video classification
Authors: Shiv Naga Prasad Vitaladevuni, Pradeep Natarajan, Rohit Prasad, Prem Natarajan
Venue: ICCV
Year: 2011

Sparse projection has been shown to be highly effective in several domains, including image denoising and scene / object classification. However, practical application to large scale problems such as video analysis requires efficient versions of sparse projection algorithms such as Orthogonal Matching Pursuit (OMP). In particular, random projection based locality sensitive hashing (LSH) has been proposed for OMP. In this paper, we propose a novel technique called Comparison Hadamard random projection (CHRP) for further improving the efficiency of LSH within OMP. CHRP combines two techniques:(1) The Fast Johnson-Lindenstrauss Transform (FJLT) which uses a randomized Hadamard transform and sparse projection matrix for LSH, and (2) Achlioptas' random projection that uses only addition and comparison operations. Our approach provides the robustness of FJLT while completely avoiding multiplications. We empirically validate CHRP's efficacy by performing a suite of experiments for image denoising, scene classification, and video categorization. Our experiments indicate that CHRP significantly speeds-up OMP with negligible loss in classification accuracy.
3D scene flow estimation with a rigid motion prior
Authors: Christoph Vogel, Konrad Schindler, Stefan Roth 0001
Venue: ICCV
Year: 2011

We present an approach to 3D scene flow estimation, which exploits that in realistic scenarios image motion is frequently dominated by observer motion and independent, but rigid object motion. We cast the dense estimation of both scene structure and 3D motion from sequences of two or more views as a single energy minimization problem. We show that agnostic smoothness priors, such as the popular total variation, are biased against motion discontinuities in viewing direction. Instead, we propose to regularize by encouraging local rigidity of the 3D scene. We derive a local rigidity constraint of the 3D scene flow and define a smoothness term that penalizes deviations from that constraint, thus favoring solutions that consist largely of rigidly moving parts. Our experiments show that the new rigid motion prior reduces the 3D flow error by 42% compared to standard TV regularization with the same data term.
Modeling temporal coherence for optical flow
Authors: Sebastian Volz, Andrés Bruhn, Levi Valgaerts, Henning Zimmer
Venue: ICCV
Year: 2011

Despite the fact that temporal coherence is undeniably one of the key aspects when processing video data, this concept has hardly been exploited in recent optical flow methods. In this paper, we will present a novel parametrization for multi-frame optical flow computation that naturally enables us to embed the assumption of a temporally coherent spatial flow structure, as well as the assumption that the optical flow is smooth along motion trajectories. While the first assumption is realized by expanding spatial regularization over multiple frames, the second assumption is imposed by two novel first- and second-order trajectorial smoothness terms. With respect to the latter, we investigate an adaptive decision scheme that makes a local (per pixel) or global (per sequence) selection of the most appropriate model possible. Experiments show the clear superiority of our approach when compared to existing strategies for imposing temporal coherence. Moreover, we demonstrate the state-of-the-art performance of our method by achieving Top 3 results at the widely used Middlebury benchmark.
Superpixel tracking
Authors: Shu Wang, Huchuan Lu, Fan Yang 0016, Ming-Hsuan Yang 0001
Venue: ICCV
Year: 2011

While numerous algorithms have been proposed for object tracking with demonstrated success, it remains a challenging problem for a tracker to handle large change in scale, motion, shape deformation with occlusion. One of the main reasons is the lack of effective image representation to account for appearance variation. Most trackers use high-level appearance structure or low-level cues for representing and matching target objects. In this paper, we propose a tracking method from the perspective of mid-level vision with structural information captured in superpixels. We present a discriminative appearance model based on superpixels, thereby facilitating a tracker to distinguish the target and the background with mid-level cues. The tracking task is then formulated by computing a target-background confidence map, and obtaining the best candidate by maximum a posterior estimate. Experimental results demonstrate that our tracker is able to handle heavy occlusion and recover from drifts. In conjunction with online update, the proposed algorithm is shown to perform favorably against existing methods for object tracking.
Dyadic transfer learning for cross-domain image classification
Authors: Hua Wang 0007, Feiping Nie 0001, Heng Huang 0001, Chris H. Q. Ding
Venue: ICCV
Year: 2011

Because manual image annotation is both expensive and labor intensive, in practice we often do not have sufficient labeled images to train an effective classifier for the new image classification tasks. Although multiple labeled image data sets are publicly available for a number of computer vision tasks, a simple mixture of them cannot achieve good performance due to the heterogeneous properties and structures between different data sets. In this paper, we propose a novel nonnegative matrix tri-factorization based transfer learning framework, called as Dyadic Knowledge Transfer (DKT) approach, to transfer cross-domain image knowledge for the new computer vision tasks, such as classifications. An efficient iterative algorithm to solve the proposed optimization problem is introduced. We perform the proposed approach on two benchmark image data sets to simulate the real world cross-domain image classification tasks. Promising experimental results demonstrate the effectiveness of the proposed approach.
Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance
Authors: Hua Wang 0007, Feiping Nie 0001, Heng Huang 0001, Shannon L. Risacher, Chris H. Q. Ding, Andrew J. Saykin, Li Shen 0001
Venue: ICCV
Year: 2011

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions, which makes regression analysis a suitable model to study whether neuroimaging measures can help predict memory performance and track the progression of AD. Existing memory performance prediction methods via regression, however, do not take into account either the interconnected structures within imaging data or those among memory scores, which inevitably restricts their predictive capabilities. To bridge this gap, we propose a novel Sparse Multi-tAsk Regression and feaTure selection (SMART) method to jointly analyze all the imaging and clinical data under a single regression framework and with shared underlying sparse representations. Two convex regularizations are combined and used in the model to enable sparsity as well as facilitate multi-task learning. The effectiveness of the proposed method is demonstrated by both clearly improved prediction performances in all empirical test cases and a compact set of selected RAVLT-relevant MRI predictors that accord with prior studies.