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Additional info for Avalanche Atlas: Illustrated International Avalanche Classification
Hence, the impact of the proposed framework is two-fold. , multimedia retrieval, time series databases, biometric databases, etc. Since applications come from diﬀerent domains outside the computer science, the contribution of our outcomes is truly multi-disciplinary.
M. Barrios, B. Bustos, and T. Skopal either updated with an ∞ distance; 2) the cell is left unmodiﬁed but before any read the query stored in the cell is matched with the last query for that column (the experimental section uses the latter). With this strategy, each row is sparse, containing at most p distances between d(qi−p , oj ) and d(qi , oj ). 2. The distance d(qi , oj ) is compared to every distance in j th row and the highest distance in the row is replaced. With this strategy, each row stores p unsorted distances between d(q1 , oj ) and d(qi , oj ).
Each slice calculates a histogram of 16 bins for R, G, and B channels, and each bin with 8-bits linear quantization, producing a vector of 192 bytes. The Keyframe (KF), reduces the frame to 11×9 pixels and uses the value for each pixel, producing a vector of 99 bytes. These descriptors are calculated for all the frames in a segment and then averaged. R is the set of reference segments (|R|=211,479 segments), and Q is the set of query segments (|Q|=2,692 segments). The correct answer for a segment q ∈ Q is the reference segment rq ∈ R for which q is a copy.