varunr14/newsummary huggingface dataset:
This paper proposes a novel robust texture descriptor based on Gaussian Markov random fields GMRFs . A spatially localized parameter estimation technique using local linear regression is performed and the distributions of local parameter estimates are constructed to formulate the texture features. The inconsistencies arising in localized parameter estimation are addressed by applying generalized inverse regularization and an estimation window size selection criterion. The texture descriptors are named as local parameter histograms LPHs and are used in texture segmentation with the k means clustering algorithm. The segmentation results on general texture datasets demonstrate that LPH descriptors significantly improve the performance of classical GMRF features and achieve better results compared to the state of the art texture descriptors based on local feature distributions. Impressive natural image segmentation results are also achieved and comparisons to the other standard natural image segmentation algorithms are also presented. LPH descriptors produce promising texture features that integrate both statistical and structural information about a texture. The region boundary localization can be further improved by integrating colour information and using advanced segmentation algorithms. 


Summary: Distributions of GMRF local parameter estimates as improved texture descriptors. Spatially localized parameter estimation using local linear regression. Approaches to overcome inconsistencies in localized parameter estimation. Proposed descriptors capture both spatial dependencies and distributions of texture.
Although most of the queries submitted to search engines are composed of a few keywords and have a length that ranges from three to six words more than 15 of the total volume of the queries are verbose introduce ambiguity and cause topic drifts . We consider verbosity a different property of queries from length since a verbose query is not necessarily long it might be succinct and a short query might be verbose . This paper proposes a methodology to automatically detect verbose queries and conditionally modify queries . The methodology proposed in this paper exploits state of the art classification algorithms combines concepts from a large linguistic database and uses a topic gisting algorithm we designed for verbose query modification purposes . Our experimental results have been obtained using the TREC Robust track collection thirty topics classified by difficulty degree four queries per topic classified by verbosity and length and human assessment of query verbosity . Our results suggest that the methodology for query modification conditioned to query verbosity detection and topic gisting is significantly effective and that query modification should be refined when topic difficulty and query verbosity are considered since these two properties interact and query verbosity is not straightforwardly related to query length . 


Summary: We consider query verbosity and query length two distinct query properties. We propose a methodology to automatically detect and process verbose queries. The methodology uses concept expansion and syntactic features for query modification. Experiments were carried out on a test collection built on TREC2004 Robust collection. Retrieval effectiveness can be significantly improved by considering query verbosity.
Extracting local keypoints and keypoint descriptions from images is a primary step for many computer vision and image retrieval applications . In the literature many researchers have proposed methods for representing local texture around keypoints with varying levels of robustness to photometric and geometric transformations . Gradient based descriptors such as the Scale Invariant Feature Transform are among the most consistent and robust descriptors . The SIFT descriptor a 128 element vector consisting of multiple gradient histograms computed from local image patches around a keypoint is widely considered as the gold standard keypoint descriptor . However SIFT descriptors require at least 128bytes of storage per descriptor . Since images are typically described by thousands of keypoints it may require more space to store the SIFT descriptors for an image than the original image itself . This may be prohibitive in extremely large scale applications and applications on memory constrained devices such as tablets and smartphones . In this paper with the goal of reducing the memory requirements of keypoint descriptors such as SIFT without affecting their performance we propose BIG OH a simple yet extremely effective method for binary quantization of any descriptor based on gradient orientation histograms . BIG OH s memory requirements are very small when it uses SIFT s default parameters for the construction of the gradient orientation histograms it only requires 16bytes per descriptor . BIG OH quantizes gradient orientation histograms by computing a bit vector representing the relative magnitudes of local gradients associated with neighboring orientation bins . In a series of experiments on keypoint matching with different types of keypoint detectors under various photometric and geometric transformations we find that the quantized descriptor has performance comparable to or better than other descriptors including BRISK CARD BRIEF D BRIEF SQ and PCA SIFT . Our experiments also show that BIG OH is extremely effective for image retrieval with modestly better performance than SIFT . BIG OH s drastic reduction in memory requirements obtained while preserving or improving the image matching and image retrieval performance of SIFT makes it an excellent descriptor for large image databases and applications running on memory constrained devices . 


Summary: BIG OH binary quantization of gradient orientation based descriptors is proposed. Quantized SIFT descriptors reduce memory by 88 compared to classical SIFT. BIG OH has performance comparable to SIFT and GLOH. BIG OH has better performance than BRISK CARD BRIEF and other descriptors. BIG OH is effective for large scale applications such as copy detection.
Various visual tracking approaches have been proposed for robust target tracking among which using sparse representation of the tracking target yields promising performance . Some earlier works in this line used a fixed subset of features to compress the target s appearance which has limited modeling capacity between the target and the background and could not accommodate their appearance change over long period of time . In this paper we propose a visual tracking method by modeling targets with online learned sparse features . We first extract high dimensional Haar like features as an over completed basis set and then solve the feature selection problem in an efficient L 1 regularized sparse coding process . The selected low dimensional representation best discriminates the target from its neighboring background . Next we use a naive Bayesian classifier to select the most likely target candidate by a binary classification process . The online feature selection process happens when there are significant appearance changes identified by a thresholding strategy . In this way our proposed method could work for long tracking tasks . At the same time our comprehensive experimental evaluation has shown that the proposed methods achieve excellent running speed and higher accuracy over many state of the art approaches . 


Summary: Proposed an online sparse feature selection method for modeling tracking target from its neighboring background. Introduced a correlation based feature updating strategy to accommodate significant appearance change of the target Achieved more stable and accurate tracking results compared to several state of the art methods Real time processing speed
In our paper we present an experimental study which investigated the possibility to project the need for information specialists serving knowledge workers in knowledge industries on the basis of an average university library serving their counterparts at a university . Information management functions i.e . functions and processes related to information evaluation acquisition metadata creation etc . performed in an average university library are the starting point of this investigation . The fundamental assumption is that these functions do not only occur in libraries but also in other contexts like for instance in knowledge industries . As a consequence we try to estimate the need for information professionals in knowledge industries by means of quantitative methods from library and information science and economics . Our study confirms the validity of our assumption . Accordingly the number of information specialists projected on the basis of university libraries is consistent with their actual number reported in national statistics . However in order to attain a close fit we had to revise the original research model by dismissing the split up of information specialists into reader services and technical services staff . 


Summary: Knowledge industries KI are the largest segment of the Austrian information sector. University libraries can serve as basis for estimating the need of info specialists in KI. The ratio information specialists to knowledge workers is 1 130 in KI in Austria.
Multi document summarization techniques aim to reduce documents into a small set of words or paragraphs that convey the main meaning of the original document . Many approaches to multi document summarization have used probability based methods and machine learning techniques to simultaneously summarize multiple documents sharing a common topic . However these techniques fail to semantically analyze proper nouns and newly coined words because most depend on an out of date dictionary or thesaurus . To overcome these drawbacks we propose a novel multi document summarization system called FoDoSu or Folksonomy based Multi Document Summarization that employs the tag clusters used by Flickr a Folksonomy system for detecting key sentences from multiple documents . We first create a word frequency table for analyzing the semantics and contributions of words using the HITS algorithm . Then by exploiting tag clusters we analyze the semantic relationships between words in the word frequency table . Finally we create a summary of multiple documents by analyzing the importance of each word and its semantic relatedness to others . Experimental results from the TAC 2008 and 2009 data sets demonstrate the improvement of our proposed framework over existing summarization systems . 


Summary: A novel multi document summarization system that employs the tag cluster on Flickr. The FoDoSu detects meaningful words by exploiting tag cluster for summarizing multi documents. We demonstrate the superiority of FoDoSu through experiments on TAC2008 and TAC2009.
Pedestrian detection is an important image understanding problem with many potential applications . There has been little success in creating an algorithm which exhibits a high detection rate while keeping the false alarm in a relatively low rate . This paper presents a method designed to resolve this problem . The proposed method uses the Kinect or any similar type of sensors which facilitate the extraction of a distinct foreground . Then potential regions which are candidates for the presence of human are detected by employing the widely used Histogram of Oriented Gradients technique which performs well in terms of good detection rates but suffers from significantly high false alarm rates . Our method applies a sequence of operations to eliminate the false alarms produced by the HOG detector based on investigating the fine details of local shape information . Local shape information can be identified by efficient utilization of the edge points which in this work are used to formulate the so called Shape Context model . The proposed detection framework is divided in four sequential stages with each stage aiming at refining the detection results of the previous stage . In addition our approach employs a pre evaluation stage to pre screen and restrict further detection results . Extensive experimental results on the dataset created by the authors involves 673 images collected from 11 different scenes demonstrate that the proposed method eliminates a large percentage of the false alarms produced by the HOG pedestrian detector . 


Summary: Proposed a low false alarm rate pedestrian detection method Extracted foregrounds with the employment of depth sensor Designed a pre evaluation stage to save computation time and reduce false alarm rate Built a pedestrian database with 673 depth images collected from 11 different scenes
This work deals with the challenging task of activity recognition in unconstrained videos. Standard methods are based on video encoding of low level features using Fisher Vectors or Bag of Features. However these approaches model every sequence into a single vector with fixed dimensionality that lacks any long term temporal information which may be important for recognition especially of complex activities. This work proposes a novel framework with two main technical novelties First a video encoding method that maintains the temporal structure of sequences and second a Time Flexible Kernel that allows comparison of sequences of different lengths and random alignment. Results on challenging benchmarks and comparison to previous work demonstrate the applicability and value of our framework. 


Summary: TFK a kernel framework between arbitrary length sequences. Some complex activities are defined by the order of sub actions. The new kernel framework improves results in complex activities recognition. Combination of several levels of granularity in temporal divisions reduces clutter.
We describe an Eikonal based algorithm for computing dense oversegmentation of an image often called superpixels . This oversegmentation respects local image boundaries while limiting undersegmentation . The proposed algorithm relies on a region growing scheme where the potential map used is not fixed and evolves during the diffusion . Refinement steps are also proposed to enhance at low cost the first oversegmentation . Quantitative comparisons on the Berkeley dataset show good performance on traditional metrics over current state of the art superpixel methods . 


Summary: Fast and efficient superpixel algorithm based on the Eikonal equation Dynamic color based potential function Parameter free refinement of the superpixels Equivalent or better performances than the state of the art Flexibility of the approach
The rotation scaling and translation invariant property of image moments has a high significance in image recognition . Legendre moments as a classical orthogonal moment have been widely used in image analysis and recognition . Since Legendre moments are defined in Cartesian coordinate the rotation invariance is difficult to achieve . In this paper we first derive two types of transformed Legendre polynomial substituted and weighted radial shifted Legendre polynomials . Based on these two types of polynomials two radial orthogonal moments named substituted radial shifted Legendre moments and weighted radial shifted Legendre moments are proposed . The proposed moments are orthogonal in polar coordinate domain and can be thought as generalized and orthogonalized complex moments . They have better image reconstruction performance lower information redundancy and higher noise robustness than the existing radial orthogonal moments . At last a mathematical framework for obtaining the rotation scaling and translation invariants of these two types of radial shifted Legendre moments is provided . Theoretical and experimental results show the superiority of the proposed methods in terms of image reconstruction capability and invariant recognition accuracy under both noisy and noise free conditions . 


Summary: Two types of radial Legendre moments for image analysis are proposed. The proposed moments are transformed Legendre moments. These proposed moments can be thought as orthogonalized complex moments. A framework of deriving RST invariance of the transformed moments is investigated. The proposed invariants are robust to additive white noise.
This paper deals with the problem of estimating the human upper body orientation . We propose a framework which integrates estimation of the human upper body orientation and the human movements . Our human orientation estimator utilizes a novel approach which hierarchically employs partial least squares based models of the gradient and texture features coupled with the random forest classifier . The movement predictions are done by projecting detected persons into 3D coordinates and running an Unscented Kalman Filter based tracker . The body orientation results are then fused with the movement predictions to build a more robust estimation of the human upper body orientation . We carry out comprehensive experiments and provide comparison results to show the advantages of our system over the other existing methods . 


Summary: We propose a method for estimating the human upper body orientation. Our algorithm uses partial least squares based gradient and texture feature models. Integration with an UKF based movement prediction increases the performance. Comparison with the state of the art shows the benefit of our algorithm. Experiment results using image video and camera are provided.
Recently sparse representation has been applied to object tracking where each candidate target is approximately represented as a sparse linear combination of target templates. In this paper we present a new tracking algorithm which is faster and more robust than other tracking algorithms based on sparse representation. First with an analysis of many typical tracking examples with various degrees of corruption we model the corruption as a Laplacian distribution. Then a LAD Lasso optimisation model is proposed based on Bayesian Maximum A Posteriori MAP estimation theory. Compared with L1 Tracker and APG L1 Tracker the number of optimisation variables is reduced greatly it is equal to the number of target templates regardless of the dimensions of the feature. Finally we use the Alternating Direction Method of Multipliers ADMM to solve the proposed optimisation problem. Experiments on some challenging sequences demonstrate that our proposed method performs better than the state of the art methods in terms of accuracy and robustness. 


Summary: The representation error is modelled as a Laplacian distribution. We derive our new LAD Lasso model based on a Bayesian MAP estimate. LAD Lasso model is robust to outliers. The number of optimisation variable in the new model reduces greatly. We use ADMM algorithm to solve the new optimisation problem.
This paper describes how to generate optimal projection patterns to supplement general stereo camera systems . In contrast to structured light the active stereo systems utilize the projected patterns only as auxiliary information in correspondence search whereas the structured light systems have to detect the patterns and decode them to compute depth . The concept of non recurring De Bruijn sequences is introduced and a few algorithms based on the non recurring De Bruijn sequence are designed to build optimized projection patterns for several stereo parameters . When only the search window size of a stereo system is given we show that a non recurring De Bruijn sequence with corresponding parameters makes the longest functional pattern and presents experimental results using real scenes to show the effectiveness of the proposed projection patterns . Additionally if the pattern length is given in the form of maximum disparity search range the algorithm using branch and bound search scheme to find an optimal sub sequence of a non recurring De Bruijn sequence is proposed . 


Summary: The novel concept of non recurring De Bruijn NRDB sequence The algorithms to generate NRDB sequences for given parameters The optimal subsequence search algorithm using branch and bound Discussion on similarities and differences of active stereo and structured light
This paper investigates the effects of adding texture to images with poorly textured regions on optical flow performance namely the accuracy of foreground boundary detection and computation time . Despite significant improvements in optical flow computations poor texture still remains a challenge to even the most accurate methods . Accordingly we explored the effects of simple modification of images rather than the algorithms . To localize and add texture to poorly textured regions in the background which induce the propagation of foreground optical flow we first perform a texture segmentation using Laws masks and generate a texture map . Next using a binary frame difference we constrain the poorly textured regions to those with negligible motion . Finally we calculate the optical flow for the modified images with added texture using the best optical flow methods available . It is shown that if the threshold used for binarizing the frame difference is in a specific range determined empirically variations in the final foreground detection will be insignificant . Employing the texture addition in conjunction with leading optical flow methods on multiple real and animation sequences with different texture distributions revealed considerable advantages including improvement in the accuracy of foreground boundary preservation prevention of object merging and reduction in the computation time . The F measure and the Boundary Displacement Error metrics were used to evaluate the similarity between detected and ground truth foreground masks . Furthermore preventing foreground optical flow propagation and reduction in the computation time are discussed using analysis of optical flow convergence . 


Summary: An initial step for optical flow estimation in poorly textured images is proposed. The simple yet effective step preserves motion boundaries where other methods fail. The proposed algorithm reduces computation time meaningfully. Mathematical analysis is employed to explain the advantages provided. Quantitative measures have been introduced to assess the performance.
We propose to represent a time of flight camera by the map of internal radial distances associating an intrinsic distance to each pixel as an alternative for the classic pinhole model . This representation is more general than the perspective model and appears to be a natural concept for 3D reconstruction and other applications of TOF cameras . In this new framework calibrating a ToF camera comes down to the determination of this IRD map . We show how this can be accomplished by images of flat surfaces without performing any feature detection . We prove deterministic calibration formulas using one or more plane images . We also offer a numerical optimization method that in principle needs only one image of a flat surface . This paper has been recommended for acceptance by Peter Sturm . 


Summary: We introduce a parameter free calibration model for a TOF camera IRD map . This model partially compensates certain aberrations from the pinhole model. The IRD is computed by 1 or 2 depth images of a flat surface. Introducing featureless calibration procedures for TOF sensors blank planes . Satisfying calibration for low resolution sensors.