Abstract

We consider the problem of automatic identification of faces in videos such as movies, given a dictionary of known faces from a public or an alternate database. This has applications in video indexing, content based search, surveillance, and real time recognition on wearable computers. We propose a two stage approach for this problem. First, we recognize the faces in a video using a sparse representation framework using l1-minimization and select a few key-frames based on a robust confidence measure.We then use transductive learning to propagate the labels from the key-frames to the remaining frames by incorporating constraints simultaneously in temporal and feature spaces. This is in contrast to some of the previous approaches where every test frame/track is identified independently, ignoring the correlation between the faces in video tracks. Having a few key frames belonging to few subjects for label propagation rather than a large dictionary of actors reduces the amount of confusion. We evaluate the performance of our algorithm on Movie Trailer face dataset and five movie clips, and achieve a significant improvement in labeling accuracy compared to previous approaches

Abstract

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Abstract

This paper presents a novel visual servoing controller for a satellite mounted dual-arm space robot. The controller is designed to complete the task of servoing the robot's endeffectors to the desired pose, while regulating orientation of the base-satellite. Task redundancy approach is utilized to coordinate the servoing process and attitude of the base satellite. The visual task is defined as a primary task, while regulating attitude of the base satellite to zero is defined as a secondary task. The secondary task is formulated as an optimization problem in such a way that it does not affect the primary task, and simultaneously minimizes its cost function. A set of numerical experiments are carried out on a dual-arm space robot showing efficacy of the proposed control methodology.

Abstract

Recognizing human faces in the wild is emerging as a critically important, and technically challenging computer vision problem. With a few notable exceptions, most previous works in the last several decades have focused on recognizing faces captured in a laboratory setting. However, with the intro- duction of databases such as LFW and Pubfigs, face recogni- tion community is gradually shifting its focus on much more challenging unconstrained settings. Since its introduction, LFW verification benchmark is getting a lot of attention with various researchers contributing towards state-of-the-results. To further boost the unconstrained face recognition research, we introduce a more challenging Indian Movie Face Database (IMFDB) that has much more variability compared to LFW and Pubfigs. The database consists of 34512 faces of 100 known actors collected from approximately 103 Indian movies. Unlike LFW and Pubfigs which used face detectors to automatically detect the faces from the web collection, faces in IMFDB are detected manually from all the movies. Manual selection of faces from movies resulted in high degree of variability (in scale, pose, expression, illumination, age, occlusion, makeup) which one could ever see in natural world. IMFDB is the first face database that provides a detailed annotation in terms of age, pose, gender, expression, amount of occlusion, for each face which may help other face related

Abstract

In this paper, we present a novel stable hybrid vision-based robot control algorithm. This method utilizes probabilistic integration of image-based and position-based visual servo con-trollers to produce an improved vision-based robot control. A probabilistic framework is employed to derive the integration scheme. Appropriate probabilistic importance functionsare de ned for the basic two algorithms to characterize their suitability in the task. The integrated algorithm has superior performance both in image and Cartesian spaces. Exper-iments validate this claim.

Abstract

Understanding the nature of data is the key to build-ing good representations. In domains such as natural im-ages, the data comes from very complex distributions which are hard to capture. Feature learning intends to discover or best approximate these underlying distributions and use their knowledge to weed out irrelevant information, pre-serving most of the relevant information. Feature learning can thus be seen as a form of dimensionality reduction. In this paper, we describe a feature learning scheme for nat-ural images. We hypothesize that image patches do not all come from the same distribution, they lie in multiple non-linear subspaces. We propose a framework that uses KRestricted Boltzmann Machines (K-RBMS) to learn mul-tiple non-linear subspaces in the raw image space. Pro-jections of the image patches into these subspaces gives us features, which we use to build image representations.Our algorithm solves the coupled problem of finding the right non-linear subspaces in the input space and associ-ating image patches with those subspaces in an iterative EM like algorithm to minimize the overall reconstruction error. Extensive empirical results over several popular im-age classification datasets show that representations based on our framework outperform the traditional feature repre-sentations such as the SIFT based Bag-of-Words (BoW) and convolutional deep belief networks.

Abstract

The automatic discovery of distinctive parts for an ob-ject or scene class is challenging since it requires simulta-neously to learn the part appearance and also to identify the part occurrences in images. In this paper, we propose a simple, efficient, and effective method to do so. We ad-dress this problem by learning parts incrementally, starting from a single part occurrence with an Exemplar SVM. In this manner, additional part instances are discovered and aligned reliably before being considered as training exam-ples. We also propose entropy-rank curves as a means of evaluating the distinctiveness of parts shareable between categories and use them to select useful parts out of a set of candidates.We apply the new representation to the task of scene cat-egorisation on the MIT Scene 67 benchmark. We show that our method can learn parts which are significantly more in-formative and for a fraction of the cost, compared to previ-ous part-learning methods such as Singhet al. [28]. We also show that a well constructed bag of words or Fisher vector model can substantially outperform the previous state-of-the-art classification performance on this data.

Abstract

Automatically generating meaningful descriptions for images has recently emerged as an important area of re-search. In this direction, a nearest-neighbour based gener-ative phrase prediction model (PPM) proposed by (Guptaet al. 2012) was shown to achieve state-of-the-art results on PASCAL sentence dataset, thanks to the simultaneous use of three different sources of information (i.e. visual clues,corpus statistics and available descriptions). However, they do not utilize semantic similarities among the phrases that might be helpful in relating semantically similar phrases during phrase relevance prediction. In this paper, we ex-tend their model by considering inter-phrase semantic sim-ilarities. To compute similarity between two phrases, we consider similarities among their constituent words deter-mined using Word Net. We also re-formulate their objective function for parameter learning by penalizing each pair of phrases unevenly, in a manner similar to that in structured predictions. Various automatic and human evaluations are performed to demonstrate the advantage of our “semantic phrase prediction model” (SPPM) over PPM.

Abstract

We focus on the problem of mining object categories from large datasets like Google Street View images. Mining ob-ject categories in these unannotated datasets is an impor-tant and useful step to extract meaningful information. Of-ten the location and spatial extent of an object in an im-age is unknown. Mining objects in such a setting is hard.Recent methods model this problem as learning a separate classifier for each category. This is computationally expen-sive since a large number of classifiers are required to be trained and evaluated, before one can mine a concise set of meaningful objects. On the other hand, fast and efficient solutions have been proposed for the retrieval of instances(same object) from large databases. We borrow, from in-stance retrieval pipeline, its strengths and adapt it to speedup category mining. For this, we explore objects which are“near-instances”.We mine several near-instance object categories from images. Using an instance retrieval based solution, we are able to mine certain categories of near-instance objects much faster than an Exemplar SVM based solution.

Abstract

We aim to decompose a global histogram representation of an image into histograms of its associated objects and re-gions. This task is formulated as an optimization problem,given a set of linear classifiers, which can effectively dis-criminate the object categories present in the image. Our decomposition bypasses harder problems associated with accurately localizing and segmenting objects. We evaluate our method on a wide variety of composite histograms, and also compare it with MRF-based solutions. In addition to merely mea suring the accuracy of decomposition, we also show the utility of the estimated object and background his-tograms for the task of image classification on thePASCALVOC2007dataset.