WP2.T2 - Efficient image indexing both geographically and visually ATLAS – Advanced Tourism Planning System Firstly, it is mentioned that pair-wise similarity judgments between all photos in a huge dataset, is not-practical. Efficient indexing facilitates information search and organization in large datasets. Accordingly, near-neighbour retrieval is proposed using a distance threshold. This threshold can be chosen: 1. Visually 2. Geographically Visual Indexing • GIST features ▫ GIST Features were computed for each image ▫ The images were resized to 256x256 for the features computation. ▫ A 512-dimensional feature vector is given Locality Sensitive Hashing (LSH) • The LSH implementation from Andoni was used ▫ http://www.mit.edu/~andoni/LSH/ • All images are assigned to a number of hash buckets along with similar to them images. Results Geographical Indexing Nowadays, modern electronic devices provide location information about an image. The geo coordinates of an image is a valuable tool that can be used as a distance threshold. According to a simple approach that has been developed the images contained in our dataset are indexed according to their latitude and longitude. More precisely, the image dataset is split: 1. According to their latitude. (North - South regions of Greece) 2. According to their longitude. (West - East) Geographical Indexing Another a little more complex approach indexes images belonging to large city centers. The five largest cities of Greece are: 1. Athens 2. Thessaloniki 3. Patra 4. Heraklion 5. Larissa Geographical Indexing 1. Locating GPS coordinates of the centers of these cities. 2. Computing geo distances between these coordinates and the ones of each image using “Haversine formula”*. 3. Setting a different distance threshold for each city. 4. Index with numbers 1-5 images with geo-distances below that threshold. http://www.movable-type.co.uk/scripts/latlong.html Geographical Indexing Finally, an hierarchical clustering algorithm has been implemented using “Haversine formula” in order to organize our data geographically. Haversine formula: a = sin²(Δφ/2) + cos(φ1).cos(φ2).sin²(Δλ/2) c = 2.atan2(√a, √(1−a)) d = R.c where φ is latitude, λ is longitude, R is earth’s radius (mean radius = 6371km) note that angles need to be in radians to pass to trig functions!