From: Feature analysis for classification of trace fluorescent labeled protein crystallization images
 | Feature | Formulation |
---|---|---|
f 1 | Autocorrelation [40] | \( {\sum \nolimits }_{i}{\sum \nolimits }_{j}(ij)p(i,j)\) |
f 2 | Contrast [40] | \({\sum \nolimits }_{n=0}^{N_{g}-1}n^{2}\left \{ {\sum \nolimits }_{i=1}^{N_{g}} {\sum \nolimits }_{j=1}^{N_{g}}p(i,j) \left.\right | |i-j|=n\right \}\) |
f 3 | Correlation (Matlab) [43] | \({\sum \nolimits }_{i}{\sum \nolimits }_{j} \frac {(i-\mu _{x})(j-\mu _{y})p(i,j)}{\sigma _{x} \sigma _{y}}\) |
f 4 | Correlation [40] | \({\sum \nolimits }_{i}{\sum \nolimits }_{j} \frac {(ij)p(i,j)-\mu _{x}\mu _{y}}{\sigma _{x}\sigma _{y}}\) |
f 5 | Cluster prominence [41] | \({\sum \nolimits }_{i}{\sum \nolimits }_{j}\left (i + j - \mu _{x} - \mu _{y} \right)^{4} p\left (i,j \right)\) |
f 6 | Cluster shade [41] | \({\sum \nolimits }_{i}{\sum \nolimits }_{j}\left (i + j - \mu _{x} - \mu _{y} \right)^{3} p\left (i,j \right)\) |
f 7 | Dissimilarity [41] | \({\sum \nolimits }_{i}{\sum \nolimits }_{j}\left | i-j \right |\cdot p(i,j)\) |
f 8 | Energy [40] | \({\sum \nolimits }_{i}{\sum \nolimits }_{j}p(i,j)^{2}\) |
f 9 | Entropy [41] | \(-{\sum \nolimits }_{i}{\sum \nolimits }_{j}p\left (i,j \right)\log \left (p(i,j)\right)\) |
f 10 | Homogeneity (Matlab) [43] | \({\sum \nolimits }_{i}{\sum \nolimits }_{j} \frac {p(i,j)}{1+\left | i-j \right |}\) |
f 11 | Homogeneity [41] | \({\sum \nolimits }_{i}{\sum \nolimits }_{j}\frac {1}{1+\left (i-j \right)^{2}}p\left (i,j \right)\) |
f 12 | Maximum probability [41] | \(\underset {i,j}{MAX}p\left (i,j \right)\) |
f 13 | Sum of squares: Variance [40] | \({\sum \nolimits }_{i}{\sum \nolimits }_{j}(i-\mu)^{2} p(i,j)\) |
f 14 | Sum average [40] | \({\sum \nolimits }_{i=2}^{2N_{g}}i p_{x+y}(i)\) |
f 15 | Sum entropy [40] | \(-{\sum \nolimits }_{i=2}^{2N_{g}}p_{x+y}(i)\log \left \{ p_{x+y}(i) \right \}\) |
f 16 | Sum variance [40] | \({\sum \nolimits }_{i=2}^{2N_{g}}(i-f_{15})^{2}p_{x+y}(i)\) |
f 17 | Difference variance [40] | v a r(p x−y ) |
f 18 | Difference entropy [40] | \(-{\sum \nolimits }_{i=0}^{N_{g}-1}p_{x-y}(i)\log \left \{ p_{x-y}(i) \right \}\) |
f 19 | Information measure of correlation 1 [40] | \(\frac {HXY-HXY1}{max\left \{ HX,HY \right \}}\) |
f 20 | Information measure of correlation 2 [40] | (1−exp[−2(H X Y2−H X Y)])1/2 |
f 21 | Inverse difference (INV) [42] | \({\sum \nolimits }_{i}{\sum \nolimits }_{j} \frac {p(i,j)}{1+\left | i-j \right |}\) |
f 22 | Inverse difference normalized [42] | \({\sum \nolimits }_{i}{\sum \nolimits }_{j}\frac {p(i,j)}{1+\left | i-j \right |/N_{g}}\) |
f 23 | Inverse difference moment [42] | \({\sum \nolimits }_{i}{\sum \nolimits }_{j} \frac {p(i,j)}{1+((i-j)/N_{g})^{2}}\) |