From 752243d192df78cb6d0d313f6be4b3dceff8c16d Mon Sep 17 00:00:00 2001
From: aj <andrewjpack@gmail.com>
Date: Sat, 30 Nov 2019 17:56:27 +0000
Subject: [PATCH] working mahalanobis

---
 cvpr_visualsearch_pca_image.m                  | 11 +++++------
 ...ry_walk.m => cvpr_visualsearch_rand_image.m |  0
 distance/compareL1.m                           |  2 +-
 distance/compareMahalanobis.m                  | 18 +++++-------------
 util/getEdgeAngleHist.m                        |  2 +-
 5 files changed, 12 insertions(+), 21 deletions(-)
 rename cvpr_visualsearch_category_walk.m => cvpr_visualsearch_rand_image.m (100%)

diff --git a/cvpr_visualsearch_pca_image.m b/cvpr_visualsearch_pca_image.m
index c9dbda6..e41de99 100644
--- a/cvpr_visualsearch_pca_image.m
+++ b/cvpr_visualsearch_pca_image.m
@@ -27,10 +27,10 @@ DATASET_FOLDER = 'dataset';
 DESCRIPTOR_FOLDER = 'descriptors';
 %% and within that folder, another folder to hold the descriptors
 %% we are interested in working with
-DESCRIPTOR_SUBFOLDER='avgRGB';
+% DESCRIPTOR_SUBFOLDER='avgRGB';
 % DESCRIPTOR_SUBFOLDER='globalRGBhisto';
 % DESCRIPTOR_SUBFOLDER='spatialColour';
-% DESCRIPTOR_SUBFOLDER='spatialColourTexture';
+DESCRIPTOR_SUBFOLDER='spatialColourTexture';
 
 CATEGORIES = ["Farm Animal" 
     "Tree"
@@ -101,10 +101,10 @@ for iteration=1:CAT_TOTAL
     
     %% 3) Compute EigenModel
     E = getEigenModel(ALLFEAT);
-    E = deflateEigen(E, 2);
+    E = deflateEigen(E, 12);
     
     %% 4) Project data to lower dimensionality
-%     ALLFEAT=ALLFEAT-repmat(E.org,size(ALLFEAT,1),1);
+    ALLFEAT=ALLFEAT-repmat(E.org,size(ALLFEAT,1),1);
     ALLFEAT=((E.vct')*(ALLFEAT'))';
 
     %% 3) Compute the distance of image to the query
@@ -116,7 +116,7 @@ for iteration=1:CAT_TOTAL
         category=ALLCATs(i);
 
         %% COMPARE FUNCTION
-        thedst=compareMahalanobis(E, ALLFEAT, query);
+        thedst=compareMahalanobis(E, query, candidate);
         dst=[dst ; [thedst i category]];
     end
     dst=sortrows(dst,1);  % sort the results
@@ -134,7 +134,6 @@ for iteration=1:CAT_TOTAL
     end
     fprintf('category was %s\n', CATEGORIES(query_category))
     
-    
     %calculate PR for each n
     for i=1:NIMG
 
diff --git a/cvpr_visualsearch_category_walk.m b/cvpr_visualsearch_rand_image.m
similarity index 100%
rename from cvpr_visualsearch_category_walk.m
rename to cvpr_visualsearch_rand_image.m
diff --git a/distance/compareL1.m b/distance/compareL1.m
index 7160b73..1ca74ff 100644
--- a/distance/compareL1.m
+++ b/distance/compareL1.m
@@ -1,6 +1,6 @@
 function dst=compareL1(F1, F2)
 
-x=F1-F2;
+x=abs(F1-F2);
 dst=sum(x);
 
 return;
diff --git a/distance/compareMahalanobis.m b/distance/compareMahalanobis.m
index 11d1582..bfeb1b7 100644
--- a/distance/compareMahalanobis.m
+++ b/distance/compareMahalanobis.m
@@ -1,17 +1,9 @@
-function d=compareMahalanobis(E, obs, query)
+function d=compareMahalanobis(E, query, candidate)
 
-obs_translated = (obs -repmat(query, size(obs,1), 1))';
+x=query-candidate;
+x=(x.^2)./(E.val');
+x=sum(x);
 
-proj=E.vct*obs_translated;
-    
-dstsq=proj.*proj;
-
-E.val(E.val==0)=1; % check for eigenvalues of 0
-
-dst=dstsq./repmat((E.val),1,size(obs,2));
-
-d=sum(dst);
-
-d=sqrt(d);
+d=sqrt(x);
 
 return;
diff --git a/util/getEdgeAngleHist.m b/util/getEdgeAngleHist.m
index 8d973bd..f881ef8 100644
--- a/util/getEdgeAngleHist.m
+++ b/util/getEdgeAngleHist.m
@@ -19,7 +19,7 @@ for i = 1:rows
 end
 
 if size(vals, 2) == 0
-    F = zeros(1, bins)
+    F = zeros(1, bins);
 else
     F= histogram(vals, bins, 'Normalization', 'probability').Values;
 end