1,721,086 research outputs found
Spherical Hashing
No full textMany binary code encoding schemes based on hashing have been actively studied recently, since they can provide efficient similarity search, especially nearest neighbor search, and compact data representations suitable for handling large scale image databases in many computer vision problems. Existing hashing techniques encode high dimensional data points by using hyperplane-based hashing functions. In this paper we propose a novel hypersphere- based hashing function, spherical hashing, to map more spatially coherent data points into a binary code compared to hyperplane-based hashing functions. Furthermore, we propose a new binary code distance function, spherical Hamming distance, that is tailored to our hypersphere-based binary coding scheme, and design an efficient iterative optimization process to achieve balanced partitioning of data points for each hash function and independence between hashing functions. Our extensive experiments show that our spherical hashing technique significantly outperforms six state-of-the-art hashing techniques based on hyperplanes across various image benchmarks of sizes ranging from one to 75 million of GIST descriptors. The performance gains are consistent and large, up to 100% improvements. The excellent results confirm the unique merits of the proposed idea in using hyperspheres to encode proximity regions in high-dimensional spaces. Finally, our method is intuitive and easy to implement
Image Popularity Prediction in Social Media Using Sentiment and Context Features
No full textImages in social networks share different destinies: some are going to become
popular while others are going to be completely unnoticed.
In this paper we propose to use visual sentiment features together with three novel context features to predict a concise popularity score of social images.
Experiments on large scale datasets show the benefits of proposed features on the performance of image popularity prediction.
Exploiting state-of-the-art sentiment features, we report a qualitative analysis of which sentiments seem to be related to good or poor popularity.
To the best of our knowledge, this is the first work understanding specific visual sentiments that positively or negatively influence the eventual popularity of images
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Online action detection
Full text linkThe details of the work will be defined when the student reaches his destination.In online detection, the objective is to detect the start of an action in a video stream as soon as it happens. It is an important yet challenging problem. In many realistic scenarios, we need to detect the action before the action is completed. For example, in the autonomous driving system, it is crucial to detect whether the pedestrian is crossing the street well in time in order to make a decision to stop or to reduce the velocity. Online action detection is a very challenging task in many aspects. It is very hard to predict the start of action for three reasons: First, the background is very diverse. Moreover, there is only a few action instance in a very long video. Last but not least, the model only observes part of the action to predict. To address those challenges, we propose a framework for online action detection and simulate experiments on a large-scale untrimmed video dataset. With the proposed method we have obtained very competitive performance. We also proposed a new evaluation metric for online detection models: Point mean Average Precision (Point mAP), a more appropriate metric than the existing evaluation metrics that have been designed for action detection in an offline setting. We have conducted experiments on THUMOS'14 dataset of video analysis where our proposed model achieved the state-of-the-art performance on the online action detection task
Online action detection
No full textThe details of the work will be defined when the student reaches his destination.In online detection, the objective is to detect the start of an action in a video stream as soon as it happens. It is an important yet challenging problem. In many realistic scenarios, we need to detect the action before the action is completed. For example, in the autonomous driving system, it is crucial to detect whether the pedestrian is crossing the street well in time in order to make a decision to stop or to reduce the velocity. Online action detection is a very challenging task in many aspects. It is very hard to predict the start of action for three reasons: First, the background is very diverse. Moreover, there is only a few action instance in a very long video. Last but not least, the model only observes part of the action to predict. To address those challenges, we propose a framework for online action detection and simulate experiments on a large-scale untrimmed video dataset. With the proposed method we have obtained very competitive performance. We also proposed a new evaluation metric for online detection models: Point mean Average Precision (Point mAP), a more appropriate metric than the existing evaluation metrics that have been designed for action detection in an offline setting. We have conducted experiments on THUMOS'14 dataset of video analysis where our proposed model achieved the state-of-the-art performance on the online action detection task
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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