Loong Fah Cheong

Hierarchical spatio-temporal context modeling for action recognition

The problem of recognizing actions in realistic videos is challenging yet absorbing owing to its great potentials in many practical applications. Most previous research is limited due to the use of simplified action databases under controlled environments or focus on excessively localized features without sufficiently encapsulating the spatio-temporal context. In this paper, we propose to model the spatio-temporal context information in a hierarchical way, where three levels of context are exploited in ascending order of abstraction: 1) point-level context (SIFT average descriptor), 2) intra-trajectory context (trajectory transition descriptor), and 3) inter-trajectory context (trajectory proximity descriptor). To obtain efficient and compact representations for the latter two levels, we encode the spatiotemporal context information into the transition matrix of a Markov process, and then extract its stationary distribution as the final context descriptor. Building on the multichannel nonlinear SVMs, we validate this proposed hierarchical framework on the realistic action (HOHA) and event (LSCOM) recognition databases, and achieve 27% and 66% relative performance improvements over the state-of-the-art results, respectively. We further propose to employ the Multiple Kernel Learning (MKL) technique to prune the kernels towards speedup in algorithm evaluation.

Affective understanding in film

Affective understanding of film plays an important role in sophisticated movie analysis, ranking and indexing. However, due to the seemingly inscrutable nature of emotions and the broad affective gap from low-level features, this problem is seldom addressed. In this paper, we develop a systematic approach grounded upon psychology and cinematography to address several important issues in affective understanding. An appropriate set of affective categories are identified and steps for their classification developed. A number of effective audiovisual cues are formulated to help bridge the affective gap. In particular, a holistic method of extracting affective information from the multifaceted audio stream has been introduced. Besides classifying every scene in Hollywood domain movies probabilistically into the affective categories, some exciting applications are demonstrated. The experimental results validate the proposed approach and the efficacy of the audiovisual cues.

Smoothly varying affine stitching

Traditional image stitching using parametric transforms such as homography, only produces perceptually correct composites for planar scenes or parallax free camera motion between source frames. This limits mosaicing to source images taken from the same physical location. In this paper, we introduce a smoothly varying affine stitching field which is flexible enough to handle parallax while retaining the good extrapolation and occlusion handling properties of parametric transforms. Our algorithm which jointly estimates both the stitching field and correspondence, permits the stitching of general motion source images, provided the scenes do not contain abrupt protrusions.

Synergizing spatial and temporal texture

Temporal texture accounts for a large proportion of motion commonly experienced in the visual world. Current temporal texture techniques extract primarily motion-based features for recognition. We propose a representation where both the spatial and the temporal aspects of texture are coupled together. Such a representation has the advantages of improving efficiency as well as retaining both spatial and temporal semantics. Flow measurements form the basis of our representation. The magnitudes and directions of the normal flow are mapped as spatiotemporal textures. These textures are then aggregated over time and are subsequently analyzed by classical texture analysis tools. Such aggregation traces the history of a motion which can be useful in the understanding of motion types. By providing a spatiotemporal analysis, our approach gains several advantages over previous implementations. The strength of our approach was demonstrated in a series of experiments, including classification and comparisons with other algorithms.

Active Visual Segmentation

Attention is an integral part of the human visual system and has been widely studied in the visual attention literature. The human eyes fixate at important locations in the scene, and every fixation point lies inside a particular region of arbitrary shape and size, which can either be an entire object or a part of it. Using that fixation point as an identification marker on the object, we propose a method to segment the object of interest by finding the “optimal” closed contour around the fixation point in the polar space, avoiding the perennial problem of scale in the Cartesian space. The proposed segmentation process is carried out in two separate steps: First, all visual cues are combined to generate the probabilistic boundary edge map of the scene; second, in this edge map, the “optimal” closed contour around a given fixation point is found. Having two separate steps also makes it possible to establish a simple feedback between the mid-level cue (regions) and the low-level visual cues (edges). In fact, we propose a segmentation refinement process based on such a feedback process. Finally, our experiments show the promise of the proposed method as an automatic segmentation framework for a general purpose visual system.

Block-Sparse RPCA for Salient Motion Detection

Recent evaluation [2], [13] of representative background subtraction techniques demonstrated that there are still considerable challenges facing these methods. Challenges in realistic environment include illumination change causing complex intensity variation, background motions (trees, waves, etc.) whose magnitude can be greater than those of the foreground, poor image quality under low light, camouflage, etc. Existing methods often handle only part of these challenges; we address all these challenges in a unified framework which makes little specific assumption of the background. We regard the observed image sequence as being made up of the sum of a low-rank background matrix and a sparse outlier matrix and solve the decomposition using the Robust Principal Component Analysis method. Our contribution lies in dynamically estimating the support of the foreground regions via a motion saliency estimation step, so as to impose spatial coherence on these regions. Unlike smoothness constraint such as MRF, our method is able to obtain crisply defined foreground regions, and in general, handles large dynamic background motion much better. Furthermore, we also introduce an image alignment step to handle camera jitter. Extensive experiments on benchmark and additional challenging data sets demonstrate that our method works effectively on a wide range of complex scenarios, resulting in best performance that significantly outperforms many state-of-the-art approaches.

Activity recognition using dense long-duration trajectories

Current research on visual action/activity analysis has mostly exploited appearance-based static feature descriptions, plus statistics of short-range motion fields. The deliberate ignorance of dense, long-duration motion trajectories as features is largely due to the lack of mature mechanism for efficient extraction and quantitative representation of visual trajectories. In this paper, we propose a novel scheme for extraction and representation of dense, long-duration trajectories from video sequences, and demonstrate its ability to handle video sequences containing occlusions, camera motions, and nonrigid deformations. Moreover, we test the scheme on the KTH action recognition dataset [1], and show its promise as a scheme for general purpose long-duration motion description in realistic video sequences.

Effects of Errors in the Viewing Geometry on Shape Estimation

A sequence of images acquired by a moving sensor contains information about the three-dimensional motion of the sensor and the shape of the imaged scene. Interesting research during the past few years has attempted to characterize the errors that arise in computing 3D motion (egomotion estimation) as well as the errors that result in the estimation of the scenes structure (structure from motion). Previous research is characterized by the use of optic flow or correspondence of features in the analysis as well as by the employment of particular algorithms and models of the scene in recovering expressions for the resulting errors. This paper presents a geometric framework that characterizes the relationship between 3D motion and shape in the presence of errors. We examine how the three-dimensional space recovered by a moving monocular observer, whose 3D motion is estimated with some error, is distorted. We characterize the space of distortions by its level sets, that is, we characterize the systematic distortion via a family of iso-distortion surfaces, which describes the locus over which the depths of points in the scene in view are distorted by the same multiplicative factor. The framework introduced in this way has a number of applications: Since the visible surfaces have positive depth (visibility constraint), by analyzing the geometry of the regions where the distortion factor is negative, that is, where the visibility constraint is violated, we make explicit situations which are likely to give rise to ambiguities in motion estimation, independent of the algorithm used. We provide a uniqueness analysis for 3D motion analysis from normal flow. We study the constraints on egomotion, object motion, and depth for an independently moving object to be detectable by a moving observer, and we offer a quantitative account of the precision needed in an inertial sensor for accurate estimation of 3D motion.

Perspective Motion Segmentation via Collaborative Clustering

This paper addresses real-world challenges in the motion segmentation problem, including perspective effects, missing data, and unknown number of motions. It first formulates the 3-D motion segmentation from two perspective views as a subspace clustering problem, utilizing the epipolar constraint of an image pair. It then combines the point correspondence information across multiple image frames via a collaborative clustering step, in which tight integration is achieved via a mixed norm optimization scheme. For model selection, we propose an over-segment and merge approach, where the merging step is based on the property of the ell_1-norm of the mutual sparse representation of two over-segmented groups. The resulting algorithm can deal with incomplete trajectories and perspective effects substantially better than state-of-the-art two-frame and multi-frame methods. Experiments on a 62-clip dataset show the significant superiority of the proposed idea in both segmentation accuracy and model selection.

Semantic Segmentation without Annotating Segments

Numerous existing object segmentation frameworks commonly utilize the object bounding box as a prior. In this paper, we address semantic segmentation assuming that object bounding boxes are provided by object detectors, but no training data with annotated segments are available. Based on a set of segment hypotheses, we introduce a simple voting scheme to estimate shape guidance for each bounding box. The derived shape guidance is used in the subsequent graph-cut-based figure-ground segmentation. The final segmentation result is obtained by merging the segmentation results in the bounding boxes. We conduct an extensive analysis of the effect of object bounding box accuracy. Comprehensive experiments on both the challenging PASCAL VOC object segmentation dataset and GrabCut-50 image segmentation dataset show that the proposed approach achieves competitive results compared to previous detection or bounding box prior based methods, as well as other state-of-the-art semantic segmentation methods.