Distributed Algorithm for Parallel Edit Distance Computation

The edit distance is the measure that quantifies the difference between two strings. It is an important concept because it has its usage in many domains such as natural language processing, spell checking, genome matching, and pattern recognition. Edit distance is also known as Levenshtein distance. Sequentially, the edit distance is computed by using dynamic programming based strategy that may not provide results in reasonable time when input strings are large. In this work, a distributed algorithm is presented for parallel edit distance computation. The proposed algorithm is both time and space efficient. It is evaluated on a hybrid setup of distributed and shared memory systems. Results suggest that the proposed algorithm achieves significant performance gain over the existing parallel approach

Multi-view Human Action Recognition using Histograms of Oriented Gradients (HOG) Description of Motion History Images (MHIs)

This paper has been presented at : 13th International Conference on Frontiers of Information Technology (FIT)In this paper, a silhouette-based view-independent human action recognition scheme is proposed for multi-camera dataset. To overcome the high-dimensionality issue, incurred due to multi-camera data, the low-dimensional representation based on Motion History Image (MHI) was extracted. A single MHI is computed for each view/action video. For efficient description of MHIs Histograms of Oriented Gradients (HOG) are employed. Finally the classification of HOG based description of MHIs is based on Nearest Neighbor (NN) classifier. The proposed method does not employ feature fusion for multi-view data and therefore this method does not require a fixed number of cameras setup during training and testing stages. The proposed method is suitable for multi-view as well as single view dataset as no feature fusion is used. Experimentation results on multi-view MuHAVi-14 and MuHAVi-8 datasets give high accuracy rates of 92.65% and 99.26% respectively using Leave-One-Sequence-Out (LOSO) cross validation technique as compared to similar state-of-the-art approaches. The proposed method is computationally efficient and hence suitable for real-time action recognition systems.S.A. Velastin acknowledges funding from the Universidad Carlos III de Madrid, the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement n° 600371, el Ministerio de Economia y Competitividad (COFUND2013-51509) and Banco Santander

PMHI: Proposals From Motion History Images for Temporal Segmentation of Long Uncut Videos

This letter proposes a method for the generation of temporal action proposals for the segmentation of long uncut video sequences. The presence of consecutive multiple actions in video sequences makes the temporal segmentation a challenging problem due to the unconstrained nature of actions in space and time. To address this issue, we exploit the nonaction segments present between the actual human actions in uncut videos. From the long uncut video, we compute the energy of consecutive nonoverlapping motion history images (MHIs), which provides spatiotemporal information of motion. Our proposals from MHIs (PMHI) are based on clustering the MHIs into actions and nonaction segments by detecting minima from the energy of MHIs. PMHI efficiently segments the long uncut videos into a small number of nonoverlapping temporal action proposals. The strength of PMHI is that it is unsupervised, which alleviates the requirement for any training data. Our temporal action proposal method outperforms the existing proposal methods on the Multi-view Human Action video (MuHAVi)-uncut and Computer Vision and Pattern recognition (CVPR) 2012 Change Detection datasets with an average recall rate of 86.1% and 86.0%, respectively.Sergio A Velastin acknowledges funding by the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement nº 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santande

DA-VLAD: Discriminative action vector of locally aggregated descriptors for action recognition

This paper has been presented at : 25th IEEE International Conference on Image Processing (ICIP 2018)In this paper, we propose a novel encoding method for the representation of human action videos, that we call Discriminative Action Vector of Locally Aggregated Descriptors (DA-VLAD). DA-VLAD is motivated by the fact that there are many unnecessary and overlapping frames that cause non-discriminative codewords during the training process. DA-VLAD deals with this issue by extracting class-specific clusters and learning the discriminative power of these codewords in the form of informative weights. We use these discriminative action weights with standard VLAD encoding as a contribution of each codeword. DA-VLAD reduces the inter-class similarity efficiently by diminishing the effect of common codewords among multiple action classes during the encoding process. We present the effectiveness of DA-VLAD on two challenging action recognition datasets: UCF101 and HMDB51, improving the state-of-the-art with accuracies of 95.1% and 80.1% respectively.We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research. We also acknowledge the support from the Directorate of Advance Studies, Research and Technological development (ASR) & TD, University of Engineering and Technology Taxila, Pakistan. Sergio A Velastin acknowledges funding by the Universidad Carlos III de Madrid, the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement n 600371, el Ministerio de Economia y Competitividad (COFUND2013-51509) and Banco Santander

Multi-view human action recognition using 2D motion templates based on MHIs and their HOG description

In this study, a new multi-view human action recognition approach is proposed by exploiting low-dimensional motion information of actions. Before feature extraction, pre-processing steps are performed to remove noise from silhouettes, incurred due to imperfect, but realistic segmentation. Two-dimensional motion templates based on motion history image (MHI) are computed for each view/action video. Histograms of oriented gradients (HOGs) are used as an efficient description of the MHIs which are classified using nearest neighbor (NN) classifier. As compared with existing approaches, the proposed method has three advantages: (i) does not require a fixed number of cameras setup during training and testing stages hence missing camera-views can be tolerated, (ii) requires less memory and bandwidth requirements and hence (iii) is computationally efficient which makes it suitable for real-time action recognition. As far as the authors know, this is the first report of results on the MuHAVi-uncut dataset having a large number of action categories and a large set of camera-views with noisy silhouettes which can be used by future workers as a baseline to improve on. Experimentation results on multi-view with this dataset gives a high-accuracy rate of 95.4% using leave-one-sequence-out cross-validation technique and compares well to similar state-of-the-art approachesSergio A Velastin acknowledges the Chilean National Science and Technology Council (CONICYT) for its funding under grant CONICYT-Fondecyt Regular no. 1140209 (“OBSERVE”). He is currently funded by the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement nº 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander

The evaluation of various soil conditioners effects on the amelioration of saline-sodic soil

The soil salinity and sodicity collectively are the major problems in the soils of Pakistan and proved a continuous threat for the sustainability of agriculture.  A pot study was planned to ameliorate such problematic soils and for this purpose different soil conditioners were used viz. gypsum @ 39.078 g pot-1 soil gypsum requirement, Citric acid (CA) @ 29.067 g pot-1, H2SO4 @ 11.24 ml pot-1 and polyvinyl alcohol (PVA) @ 19.98 g pot-1 and control without any amendment and wheat was grown as a test crop. The results showed that maximum decrease in pH and SAR were 8.31 and 12.04 (mmol L-1)1/2 by application of H2SO4 and citric acid respectively. Similarly H2SO4 and citric acid treatment show significant results related to crop growth and yield. The maximum plant height (63.33cm), number of tillers (4.63), photosynthetic rate ((2.83 µmolm-2s-1), transpiration rate (0.63 molm-2s-1), stomata conductance (0.53 molm-2s-1), were by application of H2SO4. while the results related to grain yield were as maximum grain yield by H2SO4 was (15.67 g) and minimum grain yield was observed with control (6.73g). Moreover the decrease in grain yield was as H2SO4 (9.98 g) > citric acid (8.33 g) > PVA (7.36 g) > gypsum (6.12 g) > control (5.53g). From this experiment it was concluded that H2SO4 showed quick impact on soil physicochemical properties and growth parameters but gypsum and citric acid were long term and sustainable source to reclaim and to make saline-sodic soils more productive as compare to other soil conditioners. Keywords: soil conditioners, amelioration, saline-sodic soi

End-to-End Temporal Action Detection using Bag of Discriminant Snippets (BoDS)

Detecting human actions in long untrimmed videosis a challenging problem. Existing temporal action detectionmethods have difficulties in finding the precise starting andending time of the actions in untrimmed videos. In this letter, wepropose a temporal action detection framework based on a Bagof Discriminant Snippets (BoDS) that can detect multiple actionsin an end-to-end manner. BoDS is based on the observationthat multiple actions and the background classes have similarsnippets, which cause incorrect classification of action regionsand imprecise boundaries. We solve this issue by finding the keysnippetsfrom the training data of each class and compute theirdiscriminative power which is used in BoDS encoding. Duringtesting of an untrimmed video, we find the BoDS representationfor multiple candidate proposals and find their class label basedon a majority voting scheme. We test BoDS on the Thumos14 andActivityNet datasets and obtain state-of-the-art results. For thesports subset of ActivityNet dataset, we obtain a mean AveragePrecision (mAP) value of 29% at 0.7 temporal intersection overunion (tIoU) threshold. For the Thumos14 dataset, we obtain asignificant gain in terms of mAP i.e., improving from 20.8% to31.6% at tIoU=0.7.This work was supported by the ASR&TD, University of Engineering and Technology (UET) Taxila, Pakistan. The work of S. A. Velastin was supported by the Universidad Carlos III de Madrid, the European Unions Seventh Framework Program for research, technological development, and demonstration under Grant 600371, el Ministerio de Economia y Competitividad (COFUND2013-51509), and Banco Santander

Fog in the Network Weather Service: A Case for Novel Approaches

A very large amount of data must be used to reasonably measure the available network bandwidth in a Grid by simply checking the time that it takes to send it across the network with TCP. The Network Weather Service (NWS) is the most common tool for obtaining transfer delay predictions from network measurements in Grids. We show that, in simple tests in a real Grid, the results that it obtains are not good enough or require heavily loading the network. The point of this study is to illustrate the need for more sophisticated and appropriately designed network measurement tools

On the Accurate Identification of Network Paths Having a Common Bottleneck

We present a new mechanism for detecting shared bottlenecks between end-to-end paths in a network. Our mechanism, which only needs one-way delays from endpoints as an input, is based on the well-known linear algebraic approach: singular value decomposition (SVD). Clusters of flows which share a bottleneck are extracted from SVD results by applying an outlier detection method. Simulations with varying topologies and different network conditions show the high accuracy of our technique