In this paper, we complement the term frequency, which is used in many bag-of-words based information retrieval models, with information about the semantic relatedness of query and document terms. Our experiments show that when employed in the standard probabilistic retrieval model BM25, the additional semantic information significantly outperforms the standard term frequency, and also improves...

Image search systems based on local descriptors typically achieve orientation invariance by aligning the patches on their dominant orientations. Albeit successful, this choice introduces too much invariance because it does not guarantee that the patches are rotated consistently. This paper introduces an aggregation strategy of local descriptors that achieves this covariance property by jointly ...

This research looks at the eeects 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 character-based and word-based indexing. The translation retrieval performance of each system connguration is evaluated empi...

A new class of applications based on visual search engines are emerging, especially on smart-phones that have evolved into powerful tools for processing images and videos. The state-of-the-art algorithms for large visual content recognition and content based similarity search today use the “Bag of Features” (BoF) or “Bag of Words” (BoW) approach. The idea, borrowed from text retrieval, enables ...

AbstractOne fundamental problem in large scale image retrieval with the bag-of-features is its lack of spatial information, which affects accuracy of image retrieval. Depending on distribution of local features in an image, we propose a novel adaptive Hilbert-scan strategy which computes weight of each path at increasingly fine resolutions. Owing to merits of this strategy, spatial information ...

“Bag of words” model, acronym extraction, authorship ascription, coordinate matching, data mining, document clustering, document frequency, document retrieval, document similarity metrics, entity extraction, hidden Markov models, hubs and authorities, information extraction, information retrieval, key-phrase assignment, key-phrase extraction, knowledge engineering, language identification, link...

This paper presents a new method for 3D shape retrieval based on the bags-of-words model along with a weak spatial constraint. First, a two-pass sampling procedure is performed to extract the local shape descriptors, based on spin images, which are used to construct a shape dictionary. Second, the model is partitioned into different regions based on the positions of the words. Then each region ...

In this paper, we present a set of simple and efficient regularized logistic regression algorithms to predict tags of music. We first vector-quantize the delta MFCC features using k-means and construct “bag-of-words” representation for each song. We then learn the parameters of these logistic regression algorithms from the “bag-of-words” vectors and ground truth labels in the training set. At t...