Bag-of-Bags of Words : Irregular graph pyramids vs spatial pyramid matching for image retrieval

With experiments on French broadcast news videos, we show that (a) the proposed NBOW2 model learns meaningful word importance weights, (b) the NBOW2 model, like the NBOW

With experiments on standard topic and sentiment classification tasks, we show that (a) our proposed model learns mean- ingful word importance for a given task (b) our model gives

In this context, the Spatial Pyramid Representation (SPR) [3] is probably the most notable work. Its principle relies on the division of an image into sub-windows and the computation

The standard steps for deriving the signature for a facial expression image using BoVW are represented in figure 1 (a), (1): keypoints localization from the image, (2):

ROC curve of the BOW performance of classification of birch pollen aper- tures using best parameters, region size of 4x4x6 and 32 visual words with local-jets descriptor.. has

Our approach for time series classification builds on two well-known methods in computer vision: local features are extracted from time series using a SIFT- based approach and a

Key-words: Weighting schemes, content-based image retrieval, bag of visual words, information retrieval, vector space model, probabilistic model, Minkowski distance, divergence

We also demonstrated the application of histogram decomposition for improving the classification performance on multiple object and PASCAL VOC 2007 datasets using an