Content-based image retrieval aims to acquire images from huge databases by analyzing their visual features like color, texture, shape, and spatial relationship. The search for superior accuracy in image retrieval has resulted in concentrating more on semantic gap reduction between the low-level features and high level human reasoning. Fuzzy theory is a prevailing methodology which helps in attaining this goal by using attributes and interpretations similar to human reasoning. The vagueness and impreciseness in image data and the retrieval process can be modeled by fuzzy sets. This chapter analyses fuzzy theoretic approaches in various stages of content-based image retrieval system. Various fuzzy-based feature descriptors are discussed along with different fuzzy classification and indexing algorithms for content-based image retrieval. This chapter also presents an overview of various fuzzy distance and similarity measures for image retrieval. A novel fuzzy theoretic retrieval for finger vein biometric images is also proposed in this chapter with experiment and analysis.
Unsupervised Content based Image Retrieval at Different Precision Level by Combining Multiple Features
