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

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 Discriminat Analysis (LDA).
1.3.3. Similarity Measure
The similarity between two images (represented by their feature values) is defined by a similarity measure. It is a measure that reflects the degree of closeness or separation of the images under consideration. In general, similarity/distance measures map the distance or similarity between the two images into a single numeric value and depend on two factors (Torres et al., 2011) as given below.
(i) the properties of the two objects
(ii) the measure itself.
Selection of similarity metrics has a direct impact on the performance of CBIR systems. The kind of feature vectors selected determines the kind of measurement that will be used to compare their similarity. If the features extracted from the images are presented as multi-dimensional points, the distances between corresponding multi-dimensional points can be calculated. Euclidean distance is the most common metric used to measure the distance between two points in multi-dimensional space (...


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...ruction and Image Retrieval).
Chapter 7, Results and Discussion, tabulates and discusses the various results obtained while testing the proposed CBIR systems. The results obtained are analyzed and discussed in this chapter. The findings of the study are summarized along with future research directions in Chapter 8, Summary and Conclusion.
The work of several researchers are quoted and used as evidence to support the concepts explained in this dissertation. All such evidences used are listed in the Bibliography of the dissertation.
This chapter provided a brief introduction to image retrieval and general framework of CBIR systems. The objectives formulated were also outlined. To achieve the objectives outlined in this chapter, a review of the previous research work was studied and the scrutinized works are summarized in the next chapter, Review of Literature.

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