The timing and temporal order are two characteristic properties that are frequently omitted in machine learning approaches, but carry crucial information. Their consideration is currently limited to algorithms that are specialized to sequential data, but it takes a projection into a vector space to employ the wealth of ML algorithms that are known and understood. Projections inevitably cause a ...

Image-Based Localization (IBL) is the problem of estimating the 3D pose of a camera with respect to a 3D representation of the scene. IBL is quite challenging in largescale environments spanning a wide variety of viewpoints, illumination, and areas where matching a query image against hundreds of thousands of 3D points becomes prone to a large number of outliers and ambiguous situations. The cu...

This paper presents a direct semantic analysis method for learning the correlation matrix between visual and textual words from socially tagged images. In the literature, to improve the traditional visual bag-of-words (BOW) representation, latent semantic analysis has been studied extensively for learning a compact visual representation, where each visual word may be related to multiple latent ...

In this paper we describe our participation in the 2009 CLEF-IP task, which was targeted at prior-art search for topic patent documents. We opted for a baseline approach to get a feeling for the specifics of the task and the documents used. Our system retrieved patent documents based on a standard bag-of-words approach for both the Main Task and the English Task. In both runs, we extracted the ...

Most text classification systems use bag-of-words representation of documents to find the classification target function. Linguistic structures such as morphology, syntax and semantic are completely neglected in the learning process. This paper proposes a new document representation that, while including its context independent sentence meaning, is able to be used by a structured kernel functio...

Topic modeling is a powerful tool to uncover hidden thematic structures of documents. Many conventional topic models represent documents as a bag-of-words, where the important linguistic structures of documents are neglected. In this paper, we propose a novel topic model that enriches text documents with collapsed typed dependency relations to effectively acquire syntactic and semantic dependen...

This article introduces a methodology for analyzing sentiment in Arabic text using a global foreign lexical source. Our method leverages the available resource in another language such as the SentiWordNet in English to the limited language resource that is Arabic. The knowledge that is taken from the external resource will be injected into the feature model whilethe machine-learning-based class...

In this paper, we use a bag-of-words of n-grams to capture a dictionary containing the most used ”words” which we will use as features. We then proceed to classify using four different classifiers and combine their results by apply a voting, a weighted voting and a classifier to obtain the real polarity of a phrase.

In the last years automatic food image understanding has become an important research challenge for the society. This is because of the serious impact that food intake has in human life. Food recognition engines, can help the monitoring of the patient diet and his food intake habits. Nevertheless, distinguish among different classes of food is not the first question for assisted dietary monitor...

Inspired by the goal to more accurately classify text, we describe an effort to map tokens and their characteristic linguistic elements into a graph and use that expressive representation to classify text phrases. We outperform the bag-of-words approach by exploiting word order and the semantic and syntactic characteristics within the phases. In this study, we map tagged corpora into a placehol...