1 research outputs found
Spatial encoding of visual words for image classification
Appearance-based bag-of-visual words (BoVW) models are employed to represent the frequency of a
vocabulary of local features in an image. Due to their versatility, they are widely popular, although they ignore the
underlying spatial context and relationships among the features. Here, we present a unified representation that
enhances BoVWs with explicit local and global structure models. Three aspects of our method should be noted in
comparison to the previous approaches. First, we use a local structure feature that encodes the spatial attributes
between a pair of points in a discriminative fashion using class-label information. We introduce a bag-of-structural
words (BoSW) model for the given image set and describe each image with this model on its coarsely
sampled relevant keypoints. We then combine the codebook histograms of BoVW and BoSW to train a classifier.
Rigorous experimental evaluations on four benchmark data sets demonstrate that the unified representation
outperforms the conventional models and compares favorably to more sophisticated scene classification techniques.This work was supported under the Australian Research
Council’s Discovery Projects funding scheme (Project
No. DP150104645) and the National Natural Science
Foundation of China (No. 61472161)