varunr14/newsummary huggingface dataset:
The Bag of Words framework is well known in image classification . In the framework there are two essential steps 1 coding which encodes local features by a visual vocabulary and 2 pooling which pools over the response of all features into image representation . Many coding and pooling methods are proposed and how to apply them better in different conditions has become a practical problem . In this paper to better use BoW in different applications we study the relation between many typical coding methods and two popular pooling methods . Specifically complete combinations of coding and pooling are evaluated based on an extremely large range of vocabulary sizes on five primary and popular datasets . Three typical ones are 15 Scenes Caltech 101 and PASCAL VOC 2007 while the other two large scale ones are Caltech 256 and ImageNet . Based on the systematic evaluation some interesting conclusions are drawn . Some conclusions are the extensions of previous viewpoints while some are different but important to understand BoW model . Based on these conclusions we provide detailed application criterions by evaluating coding and pooling based on precision efficiency and memory requirements in different applications . We hope that this study can be helpful to evaluate different coding and pooling methods the conclusions can be beneficial to better understand BoW and the application criterions can be valuable to use BoW better in different applications . 


Summary: We empirically study coding and pooling under a large range of vocabulary sizes. We provide detailed application guidelines to use BoW in practical applications. Combined with average pooling most coding methods are comparable. The best performance of max pooling is better than the one of average pooling. A saturant point exists in maximum pooling.
Modern appearance based object recognition systems typically involve feature descriptor extraction and matching stages . The extracted descriptors are expected to be robust to illumination changes and to reasonable image object transformations . Some descriptors work well for general object matching but gray scale key point based methods may be suboptimal for matching line rich color scenes objects such as cars buildings and faces . We present a Rotation and Scale Invariant Line based Color aware descriptor which allows matching of objects scenes in terms of their key lines line region properties and line spatial arrangements . An important special application is face matching since face characteristics are best captured by lines curves . We tested RSILC performance on publicly available datasets and compared it with other descriptors . Our experiments show that RSILC is more accurate in line rich object description than other well known generic object descriptors . 


Summary: A new rotation and scale invariant line based color aware descriptor is introduced. The descriptor captures both local texture color and global inter line spatial information. Experiments show that proposed descriptor is robust to rotation scale and illumination. The descriptor is compared to the well known descriptors. The proposed descriptor is more accurate on matching line rich objects such as faces.
Improper camera orientation produces convergent vertical lines and skewed horizon lines in digital pictures an a posteriori processing is then necessary to obtain appealing pictures . We show here that after accurate calibration the camera on board accelerometer can be used to automatically generate an alternative perspective view from a virtual camera leading to images with residual keystone and horizon distortions that are essentially imperceptible at visual inspection . Furthermore we describe the uncertainty on the position of each pixel in the corrected image with respect to the accelerometer noise . Experimental results show a similar accuracy for a smartphone and for a digital reflex camera . The method can find application in customer imaging devices as well as in the computer vision field especially when reference vertical and horizontal features are not easily detectable in the image . 


Summary: On board accelerometers make feasible perspective correction in digital photography. Perspective correction is obtained with no human intervention. Perspective correction can be applied also to zoom lenses. Accelerometer calibration is necessary to get high accuracy. Accelerometer pose has to be estimated carefully to get high accuracy.
Several studies of Web server workloads have hypothesized that these workloads are self similar . The explanation commonly advanced for this phenomenon is that the distribution of Web server requests may be heavy tailed . However there is another possible explanation self similarity can also arise from deterministic chaotic processes . To our knowledge this possibility has not previously been investigated and so existing studies on Web workloads lack an adequate comparison against this alternative . We conduct an empirical study of workloads from two different Web sites one public university and one private company using the largest datasets that have been described in the literature . Our study employs methods from nonlinear time series analysis to search for chaotic behavior in the web logs of these two sites . While we do find that the deterministic components are significant components in these time series we do not find evidence of chaotic behavior . Predictive modeling experiments contrasting heavy tailed with deterministic models showed that both approaches were equally effective in modeling our datasets . 


Summary: We investigate the models used in Web traffic generators. We examine deterministic chaos as an alternative explanation of self similarity. Deterministic components are found to be significant in Web session data. Deterministic and stochastic predictive models of the data are equally accurate.
We present a novel method that tackles the problem of facial landmarking in unconstrained conditions within the part based framework. Part based methods alternate the evaluation of local appearance models to produce a per point response map and a shape fitting step which finds a valid face shape that maximises the sum of the per point responses. Our approach focuses on obtaining better appearance models for the creation of the response maps and it can be used in combination with any shape fitting strategy. Local appearance models need to tackle very heterogeneous data when dealing with in the wild imagery due to factors as varying head poses facial expressions identity lighting conditions or image quality among others. Pose wise experts are typically used in this scenario so that each expert deals with more homogeneous data. However the computation cost at test time is significantly increased. Furthermore choosing the right expert is not straightforward which can lead to gross errors. We propose to dynamically select at test time the training examples used for inference. We use a global similarity measure to select the most adequate training examples for inference and create a single test sample specific expert using a localised inference technique. To illustrate the validity of these ideas we capitalise on the recently proposed use of regression to generate local appearance models. In particular we use Gaussian processes as their non parametric nature easily allows for localised regression. This novel way of constructing the response maps is combined with two state of the art standard shape fitting algorithms the popular Constrained Local Models framework and the Consensus of Exemplars method. We validate our method on two publicly available datasets as well as on a cross dataset experiment showing a considerable performance improvement of the proposed approach. 


Summary: The problem of facial landmark detection in unconstrained conditions is tackled. A new way of constructing per landmark response maps is proposed. The method is based on the selection at test time of globally similar images. We use local Gaussian process regression for inference.
In sponsored search many advertisers have not achieved their expected performances while the search engine also has a large room to improve their revenue . Specifically due to the improper keyword bidding many advertisers can not survive the competitive ad auctions to get their desired ad impressions meanwhile a significant portion of search queries have no ads displayed in their search result pages even if many of them have commercial values . We propose recommending a group of relevant yet less competitive keywords to an advertiser . Hence the advertiser can get the chance to win some ad slots and accumulate a number of impressions . At the same time the revenue of the search engine can also be boosted since many empty ad shots are filled . Mathematically we model the problem as a mixed integer programming problem which maximizes the advertiser revenue and the relevance of the recommended keywords while minimizing the keyword competitiveness subject to the bid and budget constraints . By solving the problem we can offer an optimal group of keywords and their optimal bid prices to an advertiser . Simulation results have shown the proposed method is highly effective in increasing ad impressions expected clicks advertiser revenue and search engine revenue . 


Summary: We perform a comprehensive study on keyword bidding in sponsored search. We propose a competitiveness and relevance based bid keyword suggestion method. We model bid keyword suggestion as a mixed integer optimization problem. Experiments demonstrate the effectiveness of our proposed method.
People re identification has been a very active research topic recently in computer vision . It is an important application in surveillance systems with disjoint cameras . In this paper a framework is proposed to extract descriptors of people in videos which are based on soft biometric traits and can be further used for people re identification or other applications . Soft biometric based description is more invariant to changing factors than directly using low level features such as color and texture . The ensemble of a set of soft biometric traits can achieve good performance in people re identification . In the proposed method the body of detected people is divided into three parts and the selected soft biometric traits are extracted from each part . All traits are then combined to form the final descriptor and people re identification is performed based on the descriptor and Nearest Neighbor matching strategy . The experiments are carried out on SAIVT SoftBio database which consists of videos from disjoint surveillance cameras as well as some static image based datasets . An open ID recognition problem is also evaluated for the proposed method . Comparisons with some state of the art methods are provided as well . The experiment results show the good performance of the proposed framework . 


Summary: No training or camera calibration is needed and achieve real time processing. Extract descriptors from targets with rotations and scale change. Descriptors of partially visible people can also be extracted. No high resolution cameras are needed.
Privacy preserving collaborative filtering is an emerging web adaptation tool to cope with information overload problem without jeopardizing individuals privacy . However collaborative filtering with privacy schemes commonly suffer from scalability and sparseness as the content in the domain proliferates . Moreover applying privacy measures causes a distortion in collected data which in turn defects accuracy of such systems . In this work we propose a novel privacy preserving collaborative filtering scheme based on bisecting k means clustering in which we apply two preprocessing methods . The first preprocessing scheme deals with scalability problem by constructing a binary decision tree through a bisecting k means clustering approach while the second produces clones of users by inserting pseudo self predictions into original user profiles to boost accuracy of scalability enhanced structure . Sparse nature of collections are handled by transforming ratings into item features based profiles . After analyzing our scheme with respect to privacy and supplementary costs we perform experiments on benchmark data sets to evaluate it in terms of accuracy and online performance . Our empirical outcomes verify that combined effects of the proposed preprocessing schemes relieve scalability and augment accuracy significantly . 


Summary: A novel bisecting k means clustering based privacy preserving CF scheme is proposed. A two level preprocessing scheme is suggested to enhance scalability and accuracy. Effects of scalability and sparseness challenges are alleviated considerably. Accuracy of the solution is significantly better than knn based CF and PPCF methods.
Accurate reconstruction of 3D geometrical shape from a set of calibrated 2D multiview images is an active yet challenging task in computer vision . The existing multiview stereo methods usually perform poorly in recovering deeply concave and thinly protruding structures and suffer from several common problems like slow convergence sensitivity to initial conditions and high memory requirements . To address these issues we propose a two phase optimization method for generalized reprojection error minimization where a generalized framework of reprojection error is proposed to integrate stereo and silhouette cues into a unified energy function . For the minimization of the function we first introduce a convex relaxation on 3D volumetric grids which can be efficiently solved using variable splitting and Chambolle projection . Then the resulting surface is parameterized as a triangle mesh and refined using surface evolution to obtain a high quality 3D reconstruction . Our comparative experiments with several state of the art methods show that the performance of TwGREM based 3D reconstruction is among the highest with respect to accuracy and efficiency especially for data with smooth texture and sparsely sampled viewpoints . 


Summary: A generalized reprojection error is proposed to fuse stereo and silhouette cues. The method composes of convex optimization and mesh based surface refinement. Insensitive to initialization and scalable for high resolution reconstruction. Good performance for data with smooth texture and sparsely sampled viewpoints.
This paper proposes to model an action as the output of a sequence of atomic Linear Time Invariant LTI systems. The sequence of LTI systems generating the action is modeled as a Markov chain where a Hidden Markov Model HMM is used to model the transition from one atomic LTI system to another. In turn the LTI systems are represented in terms of their Hankel matrices. For classification purposes the parameters of a set of HMMs one for each action class are learned via a discriminative approach. This work proposes a novel method to learn the atomic LTI systems from training data and analyzes in detail the action representation in terms of a sequence of Hankel matrices. Extensive evaluation of the proposed approach on two publicly available datasets demonstrates that the proposed method attains state of the art accuracy in action classification from the 3D locations of body joints skeleton . 


Summary: We model an action as sequence of outputs of linear time invariant LTI systems. We represent the outputs of LTI systems by means of Hankelets. We adopt an HMM to model the transitions from one LTI system to another. We formulate an inference and supervised learning formulation for our model. We also present a deep analysis of the parameter settings for our action representation.
One of the leading time of flight imaging technologies for depth sensing is based on Photonic Mixer Devices PMD . In PMD sensors each pixel samples the correlation between emitted and received light signals. Current PMD cameras compute eight correlation samples per pixel in four sequential stages to obtain depth with invariance to signal amplitude and offset variations. With motion PMD pixels capture different depths at each stage. As a result correlation samples are not coherent with a single depth producing artifacts. We propose to detect and remove motion artifacts from a single frame taken by a PMD camera. The algorithm we propose is very fast simple and can be easily included in camera hardware. We recover depth of each pixel by exploiting consistency of the correlation samples and local neighbors of the pixel. In addition our method obtains the motion flow of occluding contours in the image from a single frame. The system has been validated in real scenes using a commercial low cost PMD camera and high speed dynamics. In all cases our method produces accurate results and it highly reduces motion artifacts. 


Summary: Motion Artifacts MoArt are a non systematic error in Time of Flight ToF imaging. MoArt emerge when motion appears during the integration time. MoArt cause distorted ToF measurements depth and amplitude . We propose a single frame correction of ToF measurements with motion flow estimation. We present quantitative and qualitative results in challenging scenarios.
Downloading software via Web is a major solution for publishers to deliver their software products . In this context user interfaces for software downloading play a key role . Actually they have to allow usable interactions as well as support users in taking conscious and coherent decisions about whether to accept to download a software product or not . This paper presents different design alternatives for software download interfaces i.e . the interface that prompts the user if he wishes to actually complete its download and evaluates their ability to improve the quality of user interactions while reducing errors in user decisions . More precisely we compare Authenticode the leading software download interface for Internet Explorer to Question Answer a software download interface previously proposed by the authors Dini Foglia Prete Zanda . Furthermore we evaluate the effect of extending both interfaces by means of a reputation system similar to the eBay Feedback Forum . The results of the usability studies show that the pure Question Answer interface is the most effective in minimizing users incoherent behaviors and the differences in reputation rankings significantly influence users . Overall results suggest guidelines to design the best interface depending on the context . 


Summary: The paper deals with interfaces that prompt users to download or not a product. We compare 3 different designs of such software download interfaces. Question Answer Q A interfaces result the most effective in minimizing users incoherent behaviors. Differences in reputation rankings significantly influence users. Results suggest guidelines to design the best interface depending on the context.
Preprocessing is one of the key components in a typical text classification framework . This paper aims to extensively examine the impact of preprocessing on text classification in terms of various aspects such as classification accuracy text domain text language and dimension reduction . For this purpose all possible combinations of widely used preprocessing tasks are comparatively evaluated on two different domains namely e mail and news and in two different languages namely Turkish and English . In this way contribution of the preprocessing tasks to classification success at various feature dimensions possible interactions among these tasks and also dependency of these tasks to the respective languages and domains are comprehensively assessed . Experimental analysis on benchmark datasets reveals that choosing appropriate combinations of preprocessing tasks rather than enabling or disabling them all may provide significant improvement on classification accuracy depending on the domain and language studied on . 


Summary: The impact of preprocessing on text classification in terms of various aspects is extensively examined. Experiments are conducted on two different domains and in two different languages. Choosing appropriate preprocessing tasks may improve classification accuracy significantly.
This paper introduces an action recognition system based on a multiscale local part model . This model includes both a coarse primitive level root patch covering local global information and higher resolution overlapping part patches incorporating local structure and temporal relations . Descriptors are then computed over the local part models by applying fast random sampling at very high density . We also improve the recognition performance using a discontinuity preserving optical flow algorithm . The evaluation shows that the feature dimensions can be reduced by 7 8 through PCA while preserving high accuracy . Our system achieves state of the art results on large challenging realistic datasets namely 61.0 on HMDB51 92.0 on UCF50 86.6 on UCF101 and 65.3 on Hollywood2 . 


Summary: We propose a new local part model for action recognition. A feature sampling strategy with high feature density is used. We explore and prove the benefits of using accurate optical flow algorithm for action recognition. High performance and fast action recognition are achieved.
The process of creating a photo product such as a photobook calendar or collage from a large personal image collection requires intensive user effort . The primary goal of the current research was to develop an end to end solution to the problem of photo product generation that enables the user to complete the process with minimal edits where the system intelligently selects assets and groups them before presenting the output to the user . The automation is driven by metadata extracted both from individual images as well as from sets of assets in a collection . In particular we use an automatically detected event hierarchy to establish meaningful groupings in the assets and to determine an appropriate grouping and pagination for the final product . We propose a novel intermediate construct called a storyboard which can be translated to different product types without recomputing the underlying metadata . In addition to chronological storyboards we also describe a novel hybrid storyboard that joins chronological image presentation with groups of images of a common theme . A pagination algorithm uses the information in the storyboard and the product constraints to generate a product . Finally the user is provided with a metadata driven editing mechanism that makes it easy to change the auto populated product . Given that the proposed system envisions user interaction in creating the final product user studies are conducted to judge the usefulness of the system where consumers use the system to generate a photobook with their own images . 


Summary: An end to end metadata driven intelligent system for automatic photobook creation A flexible intermediate story representation for creating multiple product types Automatic generation of representations for event based and theme based groupings A new pagination algorithm for mapping selected images onto photobook pages Metadata aided UI enabling users to view groupings and find alternative images