varunr14/newsummary huggingface dataset:
This paper introduces a novel topic model for learning a robust object model. In this hierarchical model the layout topic is used to capture the local relationships among a limited number of parts when the part topic is used to locate the potential part regions. Naturally an object model is represented as a probability distribution over a set of parts with certain layouts. Rather than a monolithic model our object model is composed of multiple sub category models designed to capture the significant variations in appearance and shape of an object category. Given a set of object instances with a bounding box an iterative learning process is proposed to divide them into several sub categories and learn the corresponding sub category models without any supervision. Through an experiment in object detection the learned object model is examined and the results highlight the advantages of our present method compared with others. 


Summary: The ideas of sub category and part based are used to learn a robust object model. A novel object representation is proposed based on the topic model. An iterative learning process is presented under the semi supervision way.
This paper proposes a new method to extract a gait feature from a raw gait video directly. The Space Time Interest Points STIPs are detected where there are significant movements of human body along both spatial and temporal directions in local spatio temporal volumes of a raw gait video. Then a histogram of STIP descriptors HSD is constructed as a gait feature. In the classification stage the support vector machine SVM is applied to recognize gaits based on HSDs. In this study the standard multi class i.e. multiple subjects classification can often be computationally infeasible at test phase when gait recognition is performed by using every possible classifiers i.e. SVM models trained for all individual subjects. In this paper the attribute based classification is applied to reduce the number of SVM models needed for recognizing each probe gait. This process will significantly reduce the test time computational complexity and also retain or even improve the recognition accuracy. When compared with other existing methods in the literature the proposed method is shown to have the promising performance for the case of normal walking and the outstanding performance for the cases of walking with variations such as walking with carrying a bag and walking with varying a type of clothes. 


Summary: A new gait feature is extracted from a raw gait video directly. The proposed gait feature extraction is performed in the spatio temporal domain. The attribute based learning is used to enhance the SVM based gait classification. The proposed method has outstanding performances under changes of clothing types. The proposed method has outstanding performances under changes of carry conditions.
Many studies have confirmed gait as a robust biometric feature for identification of individuals . However direction changes cause difficulties for most of the gait recognition systems due to appearance changes . This study presents an efficient multi view gait recognition method that allows curved trajectories on unconstrained paths in indoor environments . The recognition is based on volumetric analysis of the human gait to exploit most of the 3D information enclosed in it . Appearance based gait descriptors are extracted from 3D gait volumes and temporal patterns of them are classified using a Support Vector Machine with a sliding temporal window for majority voting . The proposed approach is experimentally validated on the AVA Multi View Dataset and on the Kyushu University 4D Gait Database . The results show that this new approach is able to identify people walking on curved paths . 


Summary: We propose a new model free approach for gait recognition. The recognition is achieved independently of the trajectory. Our method is based on 3D morphological analysis of gait sequences. Our approach is able to identify people walking on curved paths.
This paper proposes a novel method to address the registration of images with affine transformation . Firstly the Maximally Stable Extremal Region detection method is performed on the reference image and the image to be registered respectively . And the coarse affine transformation matrix between the two images is estimated by the matched MSER pairs . Two circular regions containing roughly the same image content are also obtained by fitting and normalizing the centroids of the matched MSERs from the two images . Secondly a scale invariant and approximate affine transformation invariant feature point detection algorithm based on the Gabor filter decomposition and phase congruency is performed on the two coarsely aligned regions and two feature point sets are achieved respectively . Finally the affine transformation matrix between the two feature point sets is obtained by using a probabilistic point set registration algorithm and the final affine transformation matrix between the reference image and the image to be registered is achieved according to the coarse affine transformation matrix and the affine transformation matrix between the two feature point sets . Several sets of experiments demonstrate that our proposed method performs competitively with the classical scale invariant feature transform method for images with scale changes and performs better than the traditional MSER and Affine SIFT methods for images with affine distortions . Moreover the proposed method shows higher computation efficiency and robustness to illumination change than some existing area based or feature based methods do . 


Summary: A combination of coarse alignment and refinement is employed in the method. Input images are coarsely aligned by fitting and normalizing the matched MSERs. A phase congruency and point set registration based refinement step is used. The method accurately and efficiently aligns images with affine distortions. The method is robust to illumination changes.
Influence theories constitute formal models that identify those individuals that are able to affect and guide their peers through their activity . There is a large body of work on developing such theories as they have important applications in viral marketing recommendations as well as information retrieval . Influence theories are typically evaluated through a manual process that can not scale to data voluminous enough to draw safe representative conclusions . To overcome this issue we introduce in this paper a formalized framework for large scale automatic evaluation of topic specific influence theories that are specialized in Twitter . Basically it consists of five conjunctive conditions that are indicative of real influence exertion the first three determine which influence theories are compatible with our framework while the other two estimate their relative effectiveness . At the core of these two conditions lies a novel metric that assesses the aggregate sentiment of a group of users and allows for estimating how close the behavior of influencers is to that of the entire community . We put our framework into practice using a large scale test bed with real data from 75 Twitter communities . In order to select the theories that can be employed in our analysis we introduce a generic two dimensional taxonomy that elucidates their functionality . With its help we ended up with five established topic specific theories that are applicable to our settings . The outcomes of our analysis reveal significant differences in their performance . To explain them we introduce a novel methodology for delving into the internal dynamics of the groups of influencers they define . We use it to analyze the implications of the selected theories and based on the resulting evidence we propose a novel partition of influence theories in three major categories with divergent performance . 


Summary: We propose a formalized evaluation framework for topic specific influence theories specialized in Twitter. We introduce a novel evaluation metric that assesses the aggregate sentiment of a group of user. We introduce a two dimensional taxonomy for classifying influence theories. We use the framework to evaluate five existing theories from literature and assess their performance.
Despite the fact that both the Efficient Market Hypothesis and Random Walk Theory postulate that it is impossible to predict future stock prices based on currently available information recent advances in empirical research have been proving the opposite by achieving what seems to be better than random prediction performance . We discuss some of the advantages of the most widely used performance metrics and conclude that is difficult to assess the external validity of performance using some of these measures . Moreover there remain many questions as to the real world applicability of these empirical models . In the first part of this study we design novel stock price prediction models based on state of the art text mining techniques to assert whether we can predict the movement of stock prices more accurately by including indicators of irrationality . Along with this we discuss which metrics are most appropriate for which scenarios in order to evaluate the models . Finally we discuss how to gain insight into text mining based stock price prediction models in order to evaluate validate and refine the models . 


Summary: Several state of the art stock prediction models are constructed. Different metrics for evaluating prediction models are discussed. Explanatory techniques are applied to gain insights into the model s decisions.
Relevance Based Language Models commonly known as Relevance Models are successful approaches to explicitly introduce the concept of relevance in the statistical Language Modelling framework of Information Retrieval . These models achieve state of the art retrieval performance in the pseudo relevance feedback task . On the other hand the field of recommender systems is a fertile research area where users are provided with personalised recommendations in several applications . In this paper we propose an adaptation of the Relevance Modelling framework to effectively suggest recommendations to a user . We also propose a probabilistic clustering technique to perform the neighbour selection process as a way to achieve a better approximation of the set of relevant items in the pseudo relevance feedback process . These techniques although well known in the Information Retrieval field have not been applied yet to recommender systems and as the empirical evaluation results show both proposals outperform individually several baseline methods . Furthermore by combining both approaches even larger effectiveness improvements are achieved . 


Summary: A new recommendation approach based on the Relevance Modelling RM of the problem is proposed. The neighbour selection problem is improved by Posterior Probabilistic Clustering PPC . Background information such as item popularity is successfully integrated by using RM models. Performance of the recommendation improves when more clusters are considered in the PPC technique. Combination of both contributions leads to an even better performance than their separate application.
In this paper we propose improved variants of the sentence retrieval method TF ISF . The improvement is achieved by using context consisting of neighboring sentences and at the same time promoting the retrieval of longer sentences . We thoroughly compare new modified TF ISF methods to the TF ISF baseline to an earlier attempt to include context into TF ISF named tfmix and to a language modeling based method that uses context and promoting retrieval of long sentences named 3MMPDS . Experimental results show that the TF ISF method can be improved using local context . Results also show that the TF ISF method can be improved by promoting the retrieval of longer sentences . Finally we show that the best results are achieved when combining both modifications . All new methods also show statistically significant better results than the other tested methods . 


Summary: We extend the TF ISF method to use local context. We extend the TF ISF method to promote retrieval of long sentences. Context and promoting retrieval of long sentences both improves sentence retrieval. We also combine using context and promoting retrieval of long sentences. It is useful to use at the same time context and promoting retrieval of long sentences.
The evaluation of the scale of an object in a cluttered background is a serious problem in computer vision . The most existing contour based approaches relevant to object detection address this problem by normalizing descriptor or multi scale searching such as sliding window searching spatial pyramid model etc . Besides Hough voting framework can predict the scale of an object according to some meaning fragments . However utilizing scale variant descriptor or complicated structure in these measures reduces the efficiency of detection . In the present paper we propose a novel shape feature called scale invariant contour segment context . This feature is based on the angle between contour line segments . It remains unchanged as scale varies . Most importantly it evaluates the scale of objects located in cluttered images and facilitates localization of the boundary of the object in unseen images simultaneously . In this way we need to focus on just the shape matching algorithm without considering the variant scale of the object in an image . This is a procedure which absolutely differs from voting and sliding window searching . We do experiments on ETHZ shape dataset Weizmann horses dataset and the bottle subset from PASCAL datasets . The results confirm that the present model of object detection based on CSC outperforms state of the art of shape based detection methods . 


Summary: We design a scale invariant shape descriptor for shape matching and object detection. A graph based segment matching algorithm is introduced. Accurate boundary of object can be detected in natural image. Compute the similarity between descriptors properly in clutter background
An analysis of the relative motion and point feature model configurations leading to solution degeneracy is presented for the case of a Simultaneous Localization and Mapping system using multicamera clusters with non overlapping fields of view . The SLAM optimization system seeks to minimize image space reprojection error and is formulated for a cluster containing any number of component cameras observing any number of point features over two keyframes . The measurement Jacobian is transformed to expose a reduced dimension representation such that the degeneracy of the system can be determined by the rank of a dense submatrix . A set of relative motions sufficient for degeneracy are identified for certain cluster configurations independent of target model geometry . Furthermore it is shown that increasing the number of cameras within the cluster and observing features across different cameras over the two keyframes reduces the size of the degenerate motion sets significantly . 


Summary: Analysis of solution degeneracy in general multicamera cluster SLAM. Decomposition of system determines conditions for degeneracy. Dense sub matrix reveals geometric structure for futher analysis. Identification of degenerate motions independent of feature constellation. Experiments demonstrate that adding features and cameras decreases degenerate set.
The article describes a reconstruction pipeline that generates piecewise planar models of man made environments using two calibrated views . The 3D space is sampled by a set of virtual cut planes that intersect the baseline of the stereo rig and implicitly define possible pixel correspondences across views . The likelihood of these correspondences being true matches is measured using signal symmetry analysis which enables to obtain profile contours of the 3D scene that become lines whenever the virtual cut planes intersect planar surfaces . The detection and estimation of these lines cuts is formulated as a global optimization problem over the symmetry matching cost and pairs of reconstructed lines are used to generate plane hypotheses that serve as input to PEARL clustering . The PEARL algorithm alternates between a discrete optimization step which merges planar surface hypotheses and discards detections with poor support and a continuous optimization step which refines the plane poses taking into account surface slant . The pipeline outputs an accurate semi dense Piecewise Planar Reconstruction of the 3D scene . In addition the input images can be segmented into piecewise planar regions using a standard labeling formulation for assigning pixels to plane detections . Extensive experiments with both indoor and outdoor stereo pairs show significant improvements over state of the art methods with respect to accuracy and robustness . 


Summary: A piecewise planar reconstruction pipeline is proposed. Line cuts are detected based on signal symmetry analysis. SymStereo and PEARL are combined for carrying the 3D reconstruction and plane fitting simultaneously. Experiments show significant improvements over state of the art methods.
Fully automatic annotation of tennis game using broadcast video is a task with a great potential but with enormous challenges . In this paper we describe our approach to this task which integrates computer vision machine listening and machine learning . At the low level processing we improve upon our previously proposed state of the art tennis ball tracking algorithm and employ audio signal processing techniques to detect key events and construct features for classifying the events . At high level analysis we model event classification as a sequence labelling problem and investigate four machine learning techniques using simulated event sequences . Finally we evaluate our proposed approach on three real world tennis games and discuss the interplay between audio vision and learning . To the best of our knowledge our system is the only one that can annotate tennis game at such a detailed level . 


Summary: Fully automatic annotation of real world tennis video State of the art tennis ball tracking algorithm An integration of computer vision machine listening and machine learning The only system that can annotate tennis game at such a detailed level
Feature pooling is a key component in modern visual classification system . However the conventional two prevailing pooling techniques namely average and max poolings are not theoretically optimal due to the unrecoverable loss of the spatial information during the statistical summarization and the underlying over simplified assumption about the feature distribution . Addressing these issues this paper proposes to generalize previous pooling methods toward a weighted norm spatial pooling function tailored for class specific feature spatial distribution . Optimizing such a pooling function toward discriminative class separability that is subject to a spatial smoothness constraint yields a so called geometric norm pooling method . Furthermore to handle the variation of object scale position which would affect not only the learning of discriminative pooling weights but also the applicability of the learned weights we propose a simple yet effective self alignment step during both learning and testing to adaptively adjust the pooling weights for individual images . Image segmentation and visual saliency map are utilized to construct a directed pixel adjacency graph . The discriminative pooling weights are diffused using random walk on the constructed graph and therefore the discriminative pooling weights are propagated onto the salient and foreground region . This leads to a robust version of GLP which can cope with the misalignment of object position and scale in images . Comprehensive experiments validate the effectiveness of the proposed GLP feature pooling framework . The proposed random walk based self alignment step can effectively alleviate the image misalignment issue and further boost classification accuracy . State of the art image classification and action recognition performances are attained on several benchmarks . 


Summary: Consider the spatial distribution information in feature pooling Handle the misalignment problems in image classification and action recognition Improve the discrimination of feature pooling in visual recognition tasks Conduct extensive experiments on visual recognition tasks
Representation of facial expressions using continuous dimensions has shown to be inherently more expressive and psychologically meaningful than using categorized emotions and thus has gained increasing attention over recent years. Many sub problems have arisen in this new field that remain only partially understood. A comparison of the regression performance of different texture and geometric features and the investigation of the correlations between continuous dimensional axes and basic categorized emotions are two of these. This paper presents empirical studies addressing these problems and it reports results from an evaluation of different methods for detecting spontaneous facial expressions within the arousal valence AV dimensional space. The evaluation compares the performance of texture features SIFT Gabor LBP against geometric features FAP based distances and the fusion of the two. It also compares the prediction of arousal and valence obtained using the best fusion method to the corresponding ground truths. Spatial distribution shift similarity and correlation are considered for the six basic categorized emotions i.e. anger disgust fear happiness sadness surprise . Using the NVIE database results show that the fusion of LBP and FAP features performs the best. The results from the NVIE and FEEDTUM databases reveal novel findings about the correlations of arousal and valence dimensions to each of six basic emotion categories. 


Summary: Compared performance of texture SIFT Gabor LBP geometry and their fusions Investigated correlation of arousal and valence dimensions to six basic emotions Novel insights into categorized emotion recognition using dimensional axes
This paper reports on an approach to the analysis of form during genre recognition recorded using eye tracking . The researchers focused on eight different types of e mail such as calls for papers newsletters and spam which were chosen to represent different genres . The study involved the collection of oculographic behavior data based on the scanpath duration and scanpath length based metric to highlight the ways in which people view the features of genres . We found that genre analysis based on purpose and form was an effective means of identifying the characteristics of these e mails . The research carried out on a group of 24 participants highlighted their interaction and interpretation of the e mail texts and the visual cues or features perceived . In addition the ocular strategies of scanning and skimming they employed for the processing of the texts by block genre and representation were evaluated . 


Summary: We testify the significance of text structure during identification categorization task. We examine scanpaths of textual genres using temporal and distance based measures. We show how the form features of a genre aid in text interpretation and use. Format and layout plays an important role in human text categorization. We are looking for evidence of skimming and scanning.