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Encoding and Indexing Fuzzy Object Shape for Image Retrieval

Encoding and Indexing Fuzzy Object Shape for Image Retrieval

Copyright: © 2018 |Pages: 31
ISBN13: 9781522537960|ISBN10: 1522537961|EISBN13: 9781522537977
DOI: 10.4018/978-1-5225-3796-0.ch004
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MLA

P. Sumathy, et al. "Encoding and Indexing Fuzzy Object Shape for Image Retrieval." Image Retrieval and Analysis Using Text and Fuzzy Shape Features: Emerging Research and Opportunities, IGI Global, 2018, pp.87-117. https://doi.org/10.4018/978-1-5225-3796-0.ch004

APA

P. Sumathy, P. Shanmugavadivu, & A. Vadivel (2018). Encoding and Indexing Fuzzy Object Shape for Image Retrieval. IGI Global. https://doi.org/10.4018/978-1-5225-3796-0.ch004

Chicago

P. Sumathy, P. Shanmugavadivu, and A. Vadivel. "Encoding and Indexing Fuzzy Object Shape for Image Retrieval." In Image Retrieval and Analysis Using Text and Fuzzy Shape Features: Emerging Research and Opportunities. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3796-0.ch004

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Abstract

The recent retrieval and indexing approaches suffer from the issues of curse of dimensionality, overlapping of vectors, need of extra parameters for clustering and not supporting incremental indexing. In this chapter, an indexing approach is introduced without enduring the above specified issues. The suggested indexing structure is dynamically rearranged based on the occurrences of pattern for merging the common patterns in low-level feature. Further, the feature is encoded using GR coding scheme and the feature database space required is reduced considerably. While there are many encoding scheme available, in this chapter GR coding is used for simplicity and its applicability. In addition, the compression ratio is discussed and numerical statistics is depicted. Overall, indexing scheme reduces the search space by following predetermined pattern for clustering. The encoding scheme reduces the size of the feature database and also achieves good precision of retrieval.

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