Serkan Kiranyaz and Moncef Gabbouj
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Transcript of Serkan Kiranyaz and Moncef Gabbouj
Hierarchical Cellular Tree: An Efficient
Indexing Scheme for Content-Based
Retrieval on Multimedia Databases
Serkan Kiranyaz and Moncef Gabbouj
Objective• To present the technique of using a
Hierarchical Cellular Tree (HCT) as an indexing scheme for content-based retrieval on multimedia databases.
Why is this technique important?• Technological hardware and network
improvements• Daily usage of Internet• Technique reduces costly I/O
operations
HCT Overview• Is a MAM(Metric Access Method)
technique.• Based off the M-tree• Is a dynamic, cell-based, hierarchical
structured indexing method• Items are partitioned based on
distances and stored within cells based on their similarity proximity• Self-organized tree implemented via
genetic programming principles
Indexing Technique Categories
SAM (spatial access method)
• (dis-)similarity distance only measured through Euclidean distance.o Not suited for
deep spanning trees
MAM (metric access method) • Support black
box approach to (dis-)similarity distance.o Allows for deep
trees• Do not support
dynamic changes*
*M-tree Similarities• Is a dynamic MAM• Has a hierarchical structure based on
the mitosis of a cello Tree grows one level upwards whenever a
split occurs at the top level• Each cell is represented by a nucleus
(except the top most cell)
M-tree Problems• Achieves a balanced tree with low I/O
cost in large datasetso Problem: Multimedia databases are seldom
balanced at all.o HCT: Cells are unbalanced and can vary in
size• Must know the size of the database
entries/Cells before building (capacity M)o Problem: All M-tree structures can hit upper
limits (size non dynamic)o HCT: Removes limit on cell size as long as
they keep a definite "compactness" measure
M-tree Problems• M-tree compactness is only measured
with respect to distance of nucleus to furthest object (covering radius)o Problem: Determining compactness this way
does not allow for dynamic sizing of cells.o HCT: Uses all cell items and their minimum
distances to the cell(instead of a single nucleus item alone), compactness is constantly being updated.
Related Work in Multimedia Databases (SAM trees)• KD-Trees
o Hierarchical tree structureo Use space-partitioning methods to divide the
feature space into predefined hyperplanes• R-Trees
o Feature space divided according to distribution of database items
o Region overlapping may occur
Related Work in Multimedia Databases (SAM trees)• R*-trees
o Improves the node splitting of R-tree by taking overlapping areas into consideration
• TV-treeo Uses telescope vectorso Authors call telescope vectors "so called
telescope vectors"o Google search does not come up with
anything meaningful for telescope vectors
Related Work in Multimedia Databases (SAM trees)• X-tree
o Avoids overlapping of region bounding boxes by using a new organization of the directory
o Boxes can still intersect at higher levels in the tree
o Paper does not go into detail on what a bounding box is (assumption bounding box = cell)
• SS-treeo Uses minimum bounding spheres instead of
boxeso Less intersects at higher levels
Related Work in Multimedia Databases (MAM trees)• vp-tree(vantage point)
o organizes feature vectors(data points) into two groups according to their similarity distances with respect to a single point(vantage point)
• mvp-tree(multiple vantage point)o assigns multiple vantage points instead of
one
HCT Structure - Cell Structure• Basic container in which similar
database items are stored.• Ground level cells contain the entire
database items• Cells carry an MST (Minimum Spanning
Tree)o Holds minimum (dis-)similarity distance of each item to other
items within the cell.o Used to determine when mitosis should occur.
Splits occur at longest branch.o This is actually very similar to MVP-tree except every cell is
treated as a vantage point. Better idea about the similarity proximity of an item.
HCT Structure - Cell Structure• Cells cannot undergo mitosis before
reaching a specific level of maturityo This works like real cellso Reason for this is not like real cells
• Nucleuso Represents the owner cell of a higher levelo Nucleus is found through MST
Item with maximum number of brancheso Nucleus is updated with every operation
performed M-tree does not do this
HCT Structure - Cell Structure• Cell Compactness
o How tight focused the clustering for items within the cell
o High variations are eliminated by using more than a single item(vantage point)
HCT Structure - Cell Structure• Cell Mitosis
o Two conditions for mitosis Maturity (Nc > Nm)
• c = number of items in cell• m = maturity minimum limit
Cell Compactness (CFc > CThrL)• CFc = Compactness feature• CThrL = current level compactness
thresholdo Cell Mitosis has no cost as the cell is simply
split by breaking longest branch
HCT Structure - Cell Structure
HCT Structure - Level Structure• Top level always single cell
o If mitosis occurs on top level, new top level is created to preserve single cell top level.
• Each level attempts to dynamically maximize compactness of cells
HCT Structure - HCT Operations• Three operations
o Cell mitosiso Item insertiono Item removal
• As stated before all three operations cause a recalculation of Compactness
HCT Structure - HCT Operations• Insert
o First performs the Pre-Emptive cell search recursively descends HCT from top to
target levelo Once target located, insert item into target
cello Perform post-processing check
Check for mitosis Recalculate compactness for single or
multiple cellso If mitosis was performed
Remove old nucleus item from higher level Consecutively call Insert for new nucleus
HCT Structure - HCT Indexing• HCT can index using any set of
available featureso Must have fusion mechanismo Must have similarity measure
• Consists of two operationso Incremental constructiono Optional periodic fitness check
HCT Structure - HCT Indexing• HCT Incremental Construction
o Takes a Database D and appends all new items contained in an Array
o If an HCT does not already exist for database D All current items of D are inserted into the
Array A new HCT body is constructed from D
o Else if an HCT does exist for database D HCT body is first loaded HCT body is updated with contents of Array
HCT Structure - HCT Indexing• HCT Fitness Check
o Aims to minimize corruption which can happen during construction of HCT body Corruption happens because the order of
items that are inserted is not handledo Outliers Check
Reduces the "crowd effect" by removing redundant minority cells• minority cells, cells with a few or one item in it
All minority cells are reintroduced into the system to see if they fit into another cell
HCT Structure - HCT Indexing
o Cell Merging If a cell merge occurs that is later deemed
as not meeting the requirements of cell compactness it can be merged.
HCT - Examples
HCT-Examples
QA