The bag of visual words model has been widely used in contentbased image retrieval. However, when it is applied to medical domain, it potentially has several limitations, e.g., some ordinary feature descriptors may not be able to capture the subtle characteristics of medical images; there is a semantic gap between the low-level features and the medical concepts; the emerging multi-modal data po...

Abstract— Bag-of-visual-words or bag-of-visterms (bov) is a common technique used to index Multimedia information with the purposes of retrieval and classification. In this work we address the problem of constructing efficient bov representations of complex shapes as are the Maya syllabic hieroglyphs. Based on retrieval experiments, we assess and evaluate the performance of several variants of ...

In this paper, we present the Bag-of-Attributes (BoA) model for video representation aiming at video event retrieval. The BoA model is based on a semantic feature space for representing videos, resulting in high-level video feature vectors. For creating a semantic space, i.e., the attribute space, we can train a classifier using a labeled image dataset, obtaining a classification model that can...

This paper presents a higher level representation for videos aiming at video genre retrieval. In video genre retrieval, there is a challenge that videos may comprise multiple categories, for instance, news videos may be composed of sports, documentary, and action. Therefore, it is interesting to encode the distribution of such genres in a compact and effective manner. We propose to create a vis...

In this paper, we compare the relative effects of segment order, segmentation and segment contiguity on the retrieval performance of a translation memory system. We take a selection of both bag-of-words and segment order-sensitive string comparison methods, and run each over both characterand word-segmented data, in combination with a range of local segment contiguity models (in the form of N-g...

In this paper, we compare the relative effects of segment order, segmentation and segment contiguity on the retrieval performance of a translation memory system. We take a selection of both bag-of-words and segment order-sensitive string comparison methods, and run each over both characterand word-segmented data, in combination with a range of local segment contiguity models (in the form of N-g...

This research looks at the effects of segment 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 segment-ordersensitive string comparison methods, and test each over character-based and wordbased indexing using n-grams of various orders. To evaluate accuracy, we propose an aut...

We query the pictures of Yahoo! News for persons and objects by using the accompanying news captions as an indexing annotation. Our aim is to find these pictures on top of the answer list in which the sought persons or objects are most prominently present. We demonstrate that an appearance or content model based on syntactic, semantic and discourse analysis of the short news text is only useful...

We present in this work a new methodology to design kernels on data which is structured with smaller components, such as text, images or sequences. This methodology is a template procedure which can be applied on most kernels on measures and takes advantage of a more detailed “bag of components” representation of the objects. To obtain such a detailed description, we consider possible decomposi...

We present in this work a new methodology to design kernels on data which is structured with smaller components, such as text, images or sequences. This methodology is a template procedure which can be applied on most kernels on measures and takes advantage of a more detailed “bag of components” representation of the objects. To obtain such a detailed description, we consider possible decomposi...