Bag of Words

DBoW Library

The library is composed of two main classes: Vocabulary and Database. The former is trained offline with numerous images, whereas the latter can be established/expanded online. Both structures can be saved in binary or text format.

Weighting

Words in the vocabulary and in bag-of-words vectors are weighted. There are four weighting measures implemented to set a word weight wi:

Term frequency (tf): $w_i = \frac{n_{id}}{n_d}$ , $n_{id}$ : number of occurrences of word i in document d, : number of words in document d.
Inverse document frequency (idf): $w_i = log(\frac{N}{N_i})$ , : number of documents, : number of documents containing word i.
Term frequency -- inverse document frequency (tf-idf): $w_i = \frac{n_{id}}{n_d} log(\frac{N}{N_i}$ .
Binary:

DBow calculates N and Ni according to the number of images provided when the vocabulary is created. These values are not changed and are independent of how many entries a Database object contains.

Scoring

A score is calculated when two vectors are compared by means of a Vocabulary or when a Database is queried. These are the metrics implemented to calculate the score s between two vectors v and w (from now on, v* and w* denote vectors normalized with the L1-norm):

Dot product:
L1-norm:
L2-norm:
Bhattacharyya coefficient:
χ² (chi-square) distance:
KL-divergence:

The default configuration when creating a vocabulary is tf-idf, L1-norm.

References

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Last updated 3 years ago

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