We present a unified method for recovering from tracking failure and closing loops in real time monocular simultaneous localisation and mapping. Within a graph-based map representation, we show that recovery and loop closing both reduce to the creation of a graph edge. We describe and implement a bag-of-words appearance model for ranking potential loop closures, and a robust method for using bo...

Online news is a major source of information for many people. The overwhelming amount of new articles published every day makes it necessary to filter out unimportant ones and detect ground breaking new articles. In this paper, we propose the use of Latent Dirichlet Allocation (LDA) to find the hidden factors of important news stories. These factors are then used to train a Support Vector Machi...

This paper studies the problem of classifying structured data sources on the Web. While prior works use all features, once extracted from search interfaces, we further refine the feature set. In our research, each search interface is treated simply as a bag-of-words. We choose a subset of words, which is suited to classify web sources, by our feature selection methods with new metrics and a nov...

As the research base of computer vision, action parsing proposes an important step for the widespread use of computer intelligent device. In real world, any different actions have some differences. We use relative distance difference between objects in different actions as identify descriptor. And proposed a Bag-of-words action parsing model based object distance. Firstly, divide the video into...

We examine lexical access preferences and constraints in computing multiword expression associations from the standpoint of a high-impact extrinsic task-based performance measure, namely semantic machine translation evaluation. In automated MT evaluation metrics, machine translations are compared against human reference translations, which are almost never worded exactly the sameway except in t...

Classifying realistic human actions in video remains challenging for existing intro-variability and inter-ambiguity in action classes. Recently, Spatial-Temporal Interest Point (STIP) based local features have shown great promise in complex action analysis. However, these methods have the limitation that they typically focus on Bag-of-Words (BoW) algorithm, which can hardly discriminate actions...

This research looks at the effects of word order and segmentation on translation retrieval performance for an experimental Japanese-English translation memory system. We implement a number of both bag-of-words and word order-sensitive similarity metrics, and test each over characterbased and word-based indexing. The translation retrieval performance of each system configuration is evaluated emp...

We propose a data structure obtained by hierarchically pooling Bag-of-Words (BoW) descriptors during a sequence of views that achieves average speedups in large-scale loop closure applications ranging from 2 to 20 times on benchmark datasets. Although simple, the method works as well as sophisticated agglomerative schemes at a fraction of the cost with minimal loss of performance.

In this paper, a novel encoding scheme combining Fisher vector and bag-of-words encodings has been proposed for recognizing action in videos. The proposed Hyper-Fisher vector encoding is sum of local Fisher vectors which are computed based on the traditional Bag-of-Words (BoW) encoding. Thus, the proposed encoding is simple and yet an effective representation over the traditional Fisher Vector ...

“Fixed-point observatory” is a prototype to support users to grasp recent trends in the fields of their interest from large-scale information. It consists of content-based categorizer, named-entity-based categorizer and multiple-document summarizer. We have evaluated the content-based categorizer, which adopts the simple “bag-of-words” model. Though the quality seems be sufficient for rough cla...