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Hierarchical Cellular Tree: An Efficient Indexing Scheme for Content-Based Retrieval on Multimedia Databases. Serkan Kiranyaz and Moncef Gabbouj. Objective.

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Hierarchical Cellular Tree: An Efficient Indexing Scheme for Content-Based Retrieval on Multimedia Databases

Serkan Kiranyaz and Moncef Gabbouj


  • 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.

    • Not suited for deep spanning trees

MAM (metric access method)

  • Support black box approach to (dis-)similarity distance.

    • 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 cell

    • 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 datasets

    • Problem: Multimedia databases are seldom balanced at all.

    • HCT: Cells are unbalanced and can vary in size

  • Must know the size of the database entries/Cells before building (capacity M)

    • Problem: All M-tree structures can hit upper limits (size non dynamic)

    • 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)

    • Problem: Determining compactness this way does not allow for dynamic sizing of cells.

    • 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

    • Hierarchical tree structure

    • Use space-partitioning methods to divide the feature space into predefined hyperplanes

  • R-Trees

    • Feature space divided according to distribution of database items

    • Region overlapping may occur

Related Work in Multimedia Databases (SAM trees)

  • R*-trees

    • Improves the node splitting of R-tree by taking overlapping areas into consideration

  • TV-tree

    • Uses telescope vectors

    • Authors call telescope vectors "so called telescope vectors"

    • Google search does not come up with anything meaningful for telescope vectors

Related Work in Multimedia Databases (SAM trees)

  • X-tree

    • Avoids overlapping of region bounding boxes by using a new organization of the directory

    • Boxes can still intersect at higher levels in the tree

    • Paper does not go into detail on what a bounding box is (assumption bounding box = cell)

  • SS-tree

    • Uses minimum bounding spheres instead of boxes

    • Less intersects at higher levels

Related Work in Multimedia Databases (MAM trees)

  • vp-tree(vantage point)

    • 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)

    • 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)

    • Holds minimum (dis-)similarity distance of each item to other items within the cell.

    • Used to determine when mitosis should occur.

      • Splits occur at longest branch.

    • 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 maturity

    • This works like real cells

    • Reason for this is not like real cells

  • Nucleus

    • Represents the owner cell of a higher level

    • Nucleus is found through MST

      • Item with maximum number of branches

    • Nucleus is updated with every operation performed

      • M-tree does not do this

HCT Structure - Cell Structure

  • Cell Compactness

    • How tight focused the clustering for items within the cell

    • High variations are eliminated by using more than a single item(vantage point)

HCT Structure - Cell Structure

  • Cell Mitosis

    • 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 threshold

    • 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

    • 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

    • Cell mitosis

    • Item insertion

    • Item removal

  • As stated before all three operations cause a recalculation of Compactness

HCT Structure - HCT Operations

  • Insert

    • First performs the Pre-Emptive cell search

      • recursively descends HCT from top to target level

    • Once target located, insert item into target cell

    • Perform post-processing check

      • Check for mitosis

      • Recalculate compactness for single or multiple cells

    • 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 features

    • Must have fusion mechanism

    • Must have similarity measure

  • Consists of two operations

    • Incremental construction

    • Optional periodic fitness check

HCT Structure - HCT Indexing

  • HCT Incremental Construction

    • Takes a Database D and appends all new items contained in an Array

    • 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

    • 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

    • Aims to minimize corruption which can happen during construction of HCT body

      • Corruption happens because the order of items that are inserted is not handled

    • 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

  • 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



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