Fuzzy Object Shape for Image Retrieval

Fuzzy Object Shape for Image Retrieval

DOI: 10.4018/978-1-5225-3796-0.ch003


Compared to color and texture, the shape is considered as an important feature for many real-time applications. In this chapter, Fuzzy Object Shape (FOS) is presented for extracting the shape information present in the images. It is further noticed that the boundary of the object is ill-defined and there is impreciseness and vagueness in the object information. The closeness of the object with well-known primitive shapes are estimated. It is known that the impreciseness can be effectively captured by fuzzy functions and FOS has offered seven fuzzy membership function for the same. The value of each fuzzy membership function are constructed as feature vector to define the properties of individual objects.
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In shape based retrieval, representing shape, similarity measure and indexing are considered as the most important issues and among them the shape representation is a challenging task. Various techniques have been proposed for representing the shapes and they are broadly classified as contour-based and region-based. While the contour based approach extracts the border information of the object shape, the region-based approach considers the internal details of the shape of the object. Compared to color and texture, the contour based shape is found to play an important role in image retrieval systems (Belongie, Malik & Puzicha, 2002). The region based methods, use moment descriptors to describe shape. Contour based shape representation only exploits shape boundary information and they are classified as global shape descriptors, shape signatures and spectral descriptors. Methods such as curvature scale space (Abbasi, Mokhtarian & Kittler, 1999) and (Mokhtarian & Mackworth, 1992) and Fourier descriptors (EI-ghazal, Basir and Belkasim, 2007) have been proposed for shape similarity assessment and shape retrieval. The shape geometry features, such as circularity, eccentricity and moments have been extracted for representing the shape (Flickner et al, 1995). Structural methods (Latecki & Lakamper, 2000) represent shapes as various disjoint parts with their relationships by making use of the data structures such as trees, graphs and strings.

All these shape based features describe the shape properties and ignores the impreciseness and vagueness present in the shape of the object. The impreciseness may be captured by using fuzzy logic approach (Colombo, Bimbo & Pala, 1999) and have advocated a syntactic construction of a compositional semantics to build the semantic representation of images. A Linguistic Expression Based Image Description (LEBID), which is a fuzzy semantics description framework has been proposed to validate its feasibility in texture image retrieval (Li, Luo & Shi, 1999). It is noticed that prior knowledge is required to describe the image and fuzzy rules.

A CBIR system is proposed for general purpose as well as face image databases using two MPEG-7 image descriptors. Several sophisticated fuzzy-rough feature selection methods are used and combines the results of these methods to obtain a prominent feature subset for image representation for a particular query (Islam et a1 2017). Fuzzy-rough upper approximation possibly adds more similar images in the relevant list from boundary region to expand the relevant list. There is a need for profile based information seeking and retrieval systems and these systems should be able to support users with their context-aware information needs. Enterprise information seeking and retrieval systems using fuzzy logic is available to user along with document profiles to model user information seeking behavior (Alhabashneh et al 2017).

It is known from above discussion that the objects present in an image are important contents and can be used in CBIR applications. Identifying and representing the shape of the object is indeed quite complex due to the uncertainties in the boundary of an object of interest. In this chapter, Fuzzy-Object-Shape is discussed to capture the shape of the object of interest along with the degree of impreciseness in the boundary information. The Fuzzy-Object-Shape information is extracted from an image that provides the similarity measure of the object(s) of interest, in comparison with the Euclidian space objects, such as square, circle, etc. For each object, the fuzzy membership values are calculated and feature vector is constructed using these values.

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