Abstract

Content based indexing is critical to the effective access of the multimedia data. To this end, visual data is often annotated with textual content for bridging the semantic gap. In this paper, we present a method to generate frame level fine grained annotations for a given video clip. Access to the frame level fine grained annotations lead to rich, dense and meaningful semantic associations between the text and video. This in turn makes the video retrieval systems more accurate. We demonstrate the use of probabilistic label consistent sparse coding and dictionary learning with a K-SVD algorithm to generate `fine grained' annotations for a class of videos - lawn tennis. The algorithm simultaneously learns a classifier and a dictionary to generate the frame level annotations for the tennis videos using available textual descriptions. The utility of the proposed algorithm is demonstrated on a publicly available tennis dataset …

Abstract

An examination of object recognition challenge leaderboards (ILSVRC, PASCAL-VOC) reveals that the top-performing classifiers typically exhibit small differences amongst themselves in terms of error rate/mAP. To better differentiate the top performers, additional criteria are required. Moreover, the (test) images, on which the performance scores are based, predominantly contain fully visible objects. Therefore, ‘harder’ test images, mimicking the challenging conditions (e.g. occlusion) in which humans routinely recognize objects, need to be utilized for benchmarking. To address the concerns mentioned above, we make two contributions. First, we systematically vary the level of local object-part content, global detail and spatial context in images from PASCAL VOC 2010 to create a new benchmarking dataset dubbed PPSS-12. Second, we propose an object-part based benchmarking procedure which quantifies classifiers’ robustness to a range of visibility and contextual settings. The benchmarking procedure relies on a semantic similarity measure that naturally addresses potential semantic granularity differences between the category labels in training and test datasets, thus eliminating manual mapping. We use our procedure on the PPSS-12 dataset to benchmark top-performing classifiers trained on the ILSVRC-2012 dataset. Our results show that the proposed benchmarking procedure enables additional differentiation among state-of-the-art object classifiers in terms of their ability to handle missing content and insufficient object detail. Given this capability for additional differentiation, our approach can potentially supplement existing benchmarking procedures used in object recognition challenge leaderboards.

Abstract

Vertical video editing is the process of digitally editing the image within the frame as opposed to horizontal video editing, which arranges the shots along a timeline. Vertical editing can be a time-consuming and error-prone process when using manual key-framing and simple interpolation. In this paper, we present a general framework for automatically computing a variety of cinematically plausible shots from a single input video suitable to the special case of live performances. Drawing on working practices in traditional cinematography, the system acts as a virtual camera assistant to the film editor, who can call novel shots in the edit room with a combination of high-level instructions and manually selected keyframes.

Abstract

We present an algorithm for automatic blur detection of document images using a novel approach based on edge intensity profiles. Our main insight is that the edge profiles are a strong indicator of the blur present in the image, with steep profiles implying sharper regions and gradual profiles implying blurred regions. Our approach first retrieves the profiles for each point of intensity transition (each edge point) along the gradient and then uses them to output a quantitative measure indicating the extent of blur in the input image. The real time performance of the proposed approach makes it suitable for most applications. Additionally, our method works for both hand written and digital documents and is agnostic to the font types and sizes, which gives it a major advantage over the currently prevalent learning based approaches. Extensive quantitative and qualitative experiments over two different datasets show that our method outperforms almost all algorithms in current state of the art by a significant margin, especially in cross dataset experiments.

Abstract

The disclosed embodiments illustrate methods and systems for processing crowd-sensed data. The method includes receiving the crowd-sensed data from a mobile device associated with a user. The crowd-sensed data corresponds to metadata of an event pertaining to an aberration in at least one of a public service, a public infrastructure, a private service, or a private infrastructure. Thereafter, the event may be prioritized based at least on a type of the event, a measure of impact of the event, or a measure of urgency to resolve the event. Further, a notification of the event may be transmitted to an organization responsible to at least resolve the event, based on the prioritizing, wherein the notification comprises at least the metadata.

Abstract

The task of automatic image annotation involves assigning relevant multiple labels/tags to query images based on their visual content. One of the key challenge in multi-label image annotation task is the class imbalance problem where frequently occurring labels suppress the participation of rarely occurring labels. In this paper, we propose to exploit the multi-scale behavior in hypergraph heat diffusion framework for the automatic image annotation task. The proposed novel technique enables to model the higher order relationship among images in the feature space and provides a multi-scale label diffusion mechanism to address the class imbalance problem in the data.

Abstract

Embodiments of a method are disclosed for computing trust index among multiple entities associated with a resource marketplace. The method includes receiving multiple inputs including interaction attributes, attribute importance factors, references to databases, and multiple entities associated with the resource marketplace. The method also includes creating a weighted-interaction graph based on the received inputs. The weighted-interaction graph includes multiple vertices representing the entities. The method further includes performing a topology-specific analysis of the weighted-interaction graph. The method furthermore includes computing Euclidean distances for each pair of vertices in the weighted-interaction graph based on the performed analysis. The method also includes determining a trust index for a first entity in the received multiple entities based on the computed Euclidean distances. The trust index includes ranking of at least one of the multiple entities with respect to the first entity. The ranking is inversely proportional to the computed Euclidean distances.

Abstract

The Simultaneous Localization and Mapping problem (SLAM) in robotics is typically modeled as a dyadic graph of relative pose measurements taken by the robot. The graph nodes store the values representing the absolute pose of the robot at a given point of time. An edge connecting two nodes represents robot movement and it stores the measurements taken by the robot sensor while moving between two nodes. The objective of the SLAM problem is to find the optimal global measurements best satisfying the noisy relative measurements [12]. This problem of optimal estimation on a graph given relative measurements is a well-studied problem within the control community, for which several results and algorithms are known [3, 4]. SLAM is generally solved as a least squares problem. Robust kernels which are less sensitive to outliers are used to deal with noise and outlier measurements. However, robust kernels tend to be dependent on initialization and can fail as the number of outliers increase. Therefore, it's important to identify and prune the outlier (noisy) measurements represented by incorrect loop closure edges for an accurate pose estimate. In this paper we propose a multi-scale Heat-Kernel analysis based loop closure edge pruning algorithm for the SLAM graph. We show that compared to other pruning algorithms, our algorithm has a substantially higher precision and recall when compared and is able to handle a large amount of outlier measurements.We have corroborated results on several publicly available datasets and several types of noise. Our algorithm is not restricted to SLAM graphs only, but has a much wider applicability to other types of geometric graphs.

Abstract

Face frontalization is the process of synthesizing a frontal view of a face, given its non-frontal view. Frontalization is used in intelligent photo editing tools and also aids in improving the accuracy of face recognition systems. For example, in the case of photo editing, faces of persons in a group photo can be corrected to look into the camera, if they are looking elsewhere. Similarly, even though recent methods in face recognition claim accuracy which surpasses that of humans in some cases, perfor- mance of recognition systems degrade when profile view of faces are given as input. One way to address this issue is to synthesize frontal views of faces before recognition. We propose a simple and efficient method to address the face frontalization problem. Our method leverages the fact that faces in general have a definite structure and can be represented in a low dimensional subspace. We employ an exemplar based approach to find the transformation that relates the profile view to the frontal view, and use it to generate realistic frontalizations. Our method does not involve estimating 3 D model of the face, which is a common approach in previous work in this area. This leads to an efficient solution, since we avoid the complexity of adding one more dimension to the problem. Our method also retains the structural information of the individual as compared to that of a recent method [4], which assumes a generic 3 D model for synthesis. We show impressive qualitative and quantitative results in comparison to the state-of-the-art in this field.

Abstract

DNA molecules tagged to metal nanoparticles, especially gold nanoparticles (AuNPs), have been shown to exhibit potential applications in designing and fabrication of novel electronic nano-devices, but the binding mechanism between gold nanoparticles and DNA bases and its implications are not completely understood. In this work, a comprehensive study to examine the effect of structural perturbations offered to DNA base pairs in terms of size expansion and adsorption on a gold cluster (Au3) has been done using density functional theory. Geometric and electronic features of these complexes provide evidences for distortion of certain base pairs depending on the binding site of the cluster. This is further substantiated via normal mode, natural bond orbital (NBO) and atoms in molecules (AIM) analyses. The natural population analysis (NPA) and NBO analysis indicate that complexation greatly affects the charge distribution on the base pairs due to charge transfer between base pair and gold cluster. This charge redistribution may offer a possibility of higher order interactions. Upon complexation, a marked decrease in the HOMO-LUMO gap is observed, which is more profound in cases where size expanded bases are involved due to the extended π-conjugation of the fused benzine rings. This study demonstrates the possibility of combining structural modifications to DNA base pairs and subsequent binding to gold nano particles to modulate and achieve molecular systems with desired optico-electronic properties.