Sentiment analysis generally uses large feature sets based on a bag-of-words approach, which results in a situation where individual features are not very informative. In addition, many data sets tend to be heavily skewed. We approach this combination of challenges by investigating feature selection in order to reduce the large number of features to those that are discriminative. We examine the...

In this paper, we summarize how the action recognition can be improved when multiple views are available. The novelty is that we explore various combination schemes within the robust and simple bag-of-words (BoW) framework, from early fusion of features to late fusion of multiple classifiers. In new experiments on the publicly available IXMAS dataset, we learn that action recognition can be imp...

In this paper we report on our participation in the CLEF-IP 2011 prior art retrieval task. We investigated whether adding syntactic information in the form of dependency triples to a bag-of-words representation could lead to improvements in patent retrieval. In our experiments, we investigated this effect on the title, abstract and first 400 words of the description section. The experiments wer...

In this paper, we introduce iBoW-LCD, a novel appearance-based loop closure detection method. The presented approach makes use of an incremental Bag-of-Words (BoW) scheme based on binary descriptors to retrieve previously seen similar images, avoiding any vocabulary training stage usually required by classic BoW models. In addition, to detect loop closures, iBoW-LCD builds on the concept of dyn...

We present the study of sentiment classification of Chinese contrast sentences in this paper, which are one of the commonly used language constructs in text. In a typical review, there are at least around 6% of such sentences. Due to the complex contrast phenomenon, it is hard to use the traditional bag-of-words to model such sentences. In this paper, we propose a Two-Layer Logistic Regression ...

The main contribution of this paper is a new method for classifying document images by combining textual and visual features repectively extracted with the Bag of Words (BoW) and the Bag of Visual Words (BoVW) techniques. While previous attempts have been showing disappointing results by combining visual and textual features with the Borda-count technique, we’re proposing here a combination thr...

In this paper we present our approach to the 2010 ImageClef PhotoAnnotation task. Based on the well-known bag-of-words approach we suggest two extensions. First, we analyzed the impact of category specific features and classifiers. In order to classify quality-related image categories we implemented a sharpness measure and use this as additional feature in the classification process. Second, we...

This paper presents a two-stage approach to summarizing multiple contrastive viewpoints in opinionated text. In the first stage, we use an unsupervised probabilistic approach to model and extract multiple viewpoints in text. We experiment with a variety of lexical and syntactic features, yielding significant performance gains over bag-of-words feature sets. In the second stage, we introduce Com...

In this paper we consider an object categorization system using local HMAX features. Two feature matching techniques are compared: the MAX technique, originally proposed in the HMAX framework, and the histogram technique originating from Bag-of-Words literature. We have found that each of these techniques have their own field of operation. The histogram technique clearly outperforms the MAX tec...