We use the histogram intersection kernel [17] for constructing the spectrum intersection kernel.
The picture was analyzed in Photoshop using the Histogram function.
Autofluorescence intensity was determined by using the histogram function.
For the similarity measure between energy histograms in each direction, we employ the histogram intersection to attain simple computation, which is defined as follows: (6).
To measure the distance between two histograms we employ the histogram intersection distance metric, defined as (7) d (H i, H j ) = 1 − ∑ k = 1 B min (H i, k, H j, k ), where H i and H j are two normalized histograms, B denotes the number of bins used in our histograms, and H i, k and H j, k represent the value of the kth bin of histogram H i and H j, respectively.
Hence, in this work, the fusion at the matching score level is employed wherein the histogram intersection (using Equation 13) and L2 − Norm (using Equations 9 and 11 for ZMmag and ZMmagPhase)/L1 − Norm (using Equation 12 for ZMcomponent) is used for evaluating the matching scores of the feature vectors obtained from the LBP/LTP and ZM descriptors, respectively.
The histogram intersection distance has been used to compare the feature vectors of query and database images for both the LBP and the LTP descriptors.
Further, we observed that for the LBP/LTP descriptor, the matching score evaluated by the histogram intersection measure gives better results than using L2 − Norm.
The histogram intersection kernel (Eq. 4) is used as the weighting function.
We chose to measure such similarity by means of the histogram intersection distance[16], which has been widely used in the past in image retrieval applications.
