The UQMG group submits three runs for instance search at TRECVid 2015 [13]: all of them are automatic runs. Instead of adopting the traditional retrieval approach, e.g., Bag-of-Visual-Word (BoVW), our approach consists of three major steps: video decomposition, feature extraction and indexing. During decomposition, video segmentation is applied and various objects are extracted. Here a visual o...

This paper proposes a graph-based readability assessment method using word coupling. Compared to the state-of-theart methods such as the readability formulae, the word-based and feature-based methods, our method develops a coupled bag-of-words model which combines the merits of word frequencies and text features. Unlike the general bag-of-words model which assumes words are independent, our mod...

The spatial pooling method such as spatial pyramid matching (SPM) is very crucial in the bag of features model used in image classification. SPM partitions the image into a set of regular grids and assumes that the spatial layout of all visual words obey the uniform distribution over these regular grids. However, in practice, we consider that different visual words should obey different spatial...

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...

Traditional wisdom holds that once documents are turned into bag-of-words (unigram count) vectors, word orders are completely lost. We introduce an approach that, perhaps surprisingly, is able to learn a bigram language model from a set of bag-of-words documents. At its heart, our approach is an EM algorithm that seeks a model which maximizes the regularized marginal likelihood of the bagof-wor...

Convolutional Neural Network (NN) has gained a lot of attention of the researchers due to its high accuracy in classification and feature learning. In this paper, we evaluated the performance of CNN used as feature for image retrieval with the gold standard feature, aka SIFT. Experiments are conducted on famous Oxford 5k data-set. The mAP of SIFT and CNN is 0.6279 and 0.5284, respectively. The ...

The Oxford team participated in the high-level feature extraction and interactive search tasks. A vision only approach was used for both tasks, with no use of the text or audio information. For the high-level feature extraction task, we used two different approaches, one using sparse and one using dense visual features to learn classifiers for all 39 required concepts, using the training data s...

This paper presents a generic framework in which images are modelled as orderless sets of weighted visual features. Each visual feature is associated with a weight factor that may inform its relevance. This framework can be applied to various bag-of-patches approaches such as the bag-of-visual-word or the Fisher kernel representations. We suggest that if dense sampling is used, different scheme...

Although syntactic features offer more specific information about the context surrounding a target word in a Word Sense Disambiguation (WSD) task, in general, they have not distinguished themselves much above positional features such as bag-of-words. In this paper we offer two methods for increasing the recall rate when using syntactic features on the WSD task by: 1) using an algorithm for disc...