High performance loop closure detection using bag of word pairs

Improved language modelling using bag of word pairs

The bag-of-words (BoW) method has been used widely in language modelling and information retrieval. A document is expressed as a group of words disregarding the grammar and the order of word information. A typical BoW method is latent semantic analysis (LSA), which maps the words and documents onto the vectors in LSA space. In this paper, the concept of BoW is extended to Bag-of-Word Pairs (BoW...

A Visual Argument for Plagiarism Detection using Word Pairs

The paper argues that the word pairs metric, which calculates the proportion of consecutive words in common between two documents, is the most suitable metric for automatically comparing documents to find possible plagiarism. The metric is shown to meet three criteria for similarity detection, namely that the process should be effective, efficient for tutors and computationally efficient. Tests...

Visual Loop-Closure Detection Method Using Average Feature Descriptors

This paper proposes a novel visual loop-closure method using average feature descriptors. The average feature descriptors are computed by averaging the descriptors of feature points at each frame. Through GPGPU (General-Purpose computing on Graphics Processing Units) technique, the proposed method selects a frame having a minimum distance with the current average feature descriptor from the ave...

High Performance Method for Skin Roughness Detection Using Raman Spectroscopy

Spatial orientations of visual word pairs to improve Bag-of-Visual-Words model

This paper presents a novel approach to incorporate spatial information in the bag-of-visual-words (BoVW) model [1, 3] for category level and scene classification. In the traditional BoVW model, feature vectors are histograms of visual words. This representation is appearance based and does not contain any information regarding the arrangement of the visual words in the 2D image space. In this ...