Traditional approaches to information retrieval are based upon representing a user's query as a bag of query terms and a document as a bag of index terms and computing a degree of similarity between the two based on the overlap or number of query terms in common between them. Our long-term approach to IR applications is based upon pre-computing semantically-based word-word similarities, work wh...

First of all, we downloaded 8 directories from Caltech 101, and used “sift.m” as a function by David Lowe to get SIFT descriptors from each image. However, it took almost forever when we used 8 directories. Therefore, we ended up using only 3 directories: anchors, elephants, and helicopters. By using function ‘sift’ we gathered many descriptors for each image. Some of the images and their SIFT ...

The SIFT framework has shown to be effective in the image classification context. In [4], we designed a Bag-of-Words approach based on an adaptation of this framework to time series classification. It relies on two steps: SIFT-based features are first extracted and quantized into words; histograms of occurrences of each word are then fed into a classifier. In this paper, we investigate techniqu...

Comparing frequency counts over texts or corpora is an important task in many applications and scientific disciplines. Given a text corpus, we want to test a hypothesis, such as “word X is frequent”, “word X has become more frequent over time”, or “word X is more frequent in male than in female speech”. For this purpose we need a null model of word frequencies. The commonly used bag-of-words mo...