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Curse of Dimensionality Makes CBIR System is Necessary for Storage and Retrieval

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This is known as 'Curse of Dimensionality', which states that the number of examples necessary to reliable generalization grows exponentially with the number of dimensions. Learn ability necessitates dimensionality reduction, which is the process of reducing the number of random features under consideration during image retrieval (Roweis and Saul, 2000).
In large multimedia databases, high-dimensional representation is computationally intensive and most users are unwilling to wait for results for a long time. Thus, for storage and retrieval efficiency concerns, dimensionality reduction in CBIR systems is necessary. Example of these techniques includes Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Linear
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1.3.4. Indexing
When manipulating massive image databases, a good indexing is necessary. Processing every single item in a database, when performing queries, is extremely inefficient and slow. When working with images, the feature vectors are used as the basis of the index. Popular multi-dimensional indexing methods include the R-tree and the R*-tree algorithms (Long et al., 2003). The Self Organizing Map (SOM) is also one of the indexing structures (Laaksonen et al., 2000). Usage of indexing techniques during searching reduces processing time and thus retrieves images quickly.
1.4. PRACTICAL APPLICATIONS OF CBIR
Research and development issues in CBIR cover a range of topics which shares with mainstream image processing and information retrieval. Some of the most important are:
• to understand image users’ needs and information-seeking behaviour
• to identify suitable ways of describing image content
• to extract features from raw images
• to provide compact storage for large image databases
• to match query and stored images in a way that reflects human similarity decisions
• to efficiently access stored images by content
• to provide usable human interfaces to CBIR systems
A wide range of possible applications for CBIR technology has been identified (Gudivada and Raghavan, 1995). This section presents
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