Place Recognition Method Using Quad Vocabulary Tree

Human Action Recognition Using Pyramid Vocabulary Tree

The bag-of-visual-words (BOVW) approaches are widely used in human action recognition. Usually, large vocabulary size of the BOVW is more discriminative for inter-class action classification while small one is more robust to noise and thus tolerant to the intra-class invariance. In this pape, we propose a pyramid vocabulary tree to model local spatio-temporal features, which can characterize th...

Scalable Object Recognition Using Hierarchical Quantization with a Vocabulary Tree

Font Recognition Using Shape-Based Quad-tree and Kd-tree Decomposition

The search for appropriate data representations and visual features for content-based image retrieval continues within the computer vision community, alongside the development of new matching and indexing techniques to facilitate fast search in large-scale image databases. In this study, we present a solution to the problem of typeface identification and character recognition in text-based imag...

Small Sample Size Face Recognition using Random Quad-Tree based Ensemble Algorithm

Certain applications such as person re-identification in camera network, surveillance photo verification, forensic identification etc. suffer from a small sample size (SSS) problem severely. Conventional face recognition methods face a great challenge on SSS as the trained feature space is overfitted to the small training set. Interest in combination of multiple base classifiers to solve the SS...

FAB-MAP: Appearance-Based Place Recognition and Mapping using a Learned Visual Vocabulary Model

We present an overview of FAB-MAP, an algorithm for place recognition and mapping developed for infrastructure-free mobile robot navigation in large environments. The system allows a robot to identify when it is revisiting a previously seen location, on the basis of imagery captured by the robot’s camera. We outline a complete probabilistic framework for the task, which is applicable even in vi...