Image Retrieval

Many aspects of image retrieval are used in this research in order to satisfy all types of user needs. The first aspect of image search includes displaying images that may contain specific objects; the user can retrieve images that contain specific features. This aspect itself has m a n y methods, “Joint Objects Search”, where the displayed image may contain specific objects together, the “Separate Objects Search”, where the displayed images may contain one o r many of the specified objects and the “Backward Objects Search”, where the users search for objects that may be contained within a specific image.

The second aspect of image search is an option which allows for a specific object to be included or excluded from retrieved images. The user can retrieve images without specifying any object; or images containing or not containing various objects. Images may also be retrieved with specific weight levels of objects, availabilities or densities.

Specifying supporting objects will help to reduce ambiguities, and retrieve the most relevant images as explained more thoroughly later on in this section.

The object’s weights, supporting factors, availabilities, densities, and other attributes, used in different search methods, provide users with a means to give priorities to selected query components in order to optimise the results of the query. Applying the values of the higher-level object’s weight, for example, will increase the value of the object recall rate. Users may want to further tailor the query results by widening the scope of the result or narrowing the result.

The approach used is to integrate our concept-based image indexing and retrieval method with the use of m a n y factors; these factors are listed and then explained below.

· The supporting objects, which help to reduce the ambiguity and retrieve the most relevant images.

· A fuzzy expert system.

· The thesaurus, which is used when the entered object name is not found in the database.

Supporting Objects

When the user is searching for an object, they enter the object name and then submit their search. Some non-relevant images may be retrieved. To avoid these images from being retrieved, the user can use supporting objects that can be either included or excluded from the displayed images.

Using a supporting object such as “Smoke”, which has a high value supporting factor for Plant (as in Factory) and low value of supporting factor for the Plant (as in Plant life), will help to reduce the ambiguity, and retrieve the most relevant images.

Below we illustrate the supporting objects and how they can be used to support the desired images. The images retrieved depend on which supporting objects are used and their relevance to the specific images.

Table 6.1 Supporting Factors

Existence of Supporting Object (S.O.) % in Image

Image Name Smoke Trees Workers Pathway Forklift

Plant (Factory) 70 15 20 3 72 Plant (Plant Life) 10 95 0 50 0

Referring to the supporting objects in Table 6.1, if the user enters Plant as the object name, all images of Plant, if it is Plant, “Factory”, or Plant, “Plant life”, will be retrieved. W h e n using supporting objects like “Smoke” a n d “Workers”, the retrieved images will be only that of the Plant “Factory” since the supporting existence values of Smoke and Workers is high for Plant “Factory” and is either absent or very low for Plant “Plant Life”. Entering Plant as the object name and using the supporting objects “Tress” a nd “Pathway”, which have a high existence value in Plant “Plant life” and a low existence value in Plant “Factory” will results in only Plant “Plant Life” images being retrieved and displayed.

All other methods of image retrieval explained later on in this chapter are additional features for the supporting objects. One method concerning image retrieval having the entered object name with supporting objects is the varying of weights or densities of the supporting objects. Another method is to retrieve images which exclude specific objects which aid narrowing down the results to optimise the search.

Linguistic Variables Notations and Ranges

In this research, the fuzzy logic is used in order to determine the Linguistic variable notations of the objects’ weights, availabilities and densities as well as some other object attributes and then facilitates their ranges. T h e linguistic variable notations and their numerical ranges are used for image retrieval. Table 6.2 provides an example of the approach to the linguistic variable notation of one of the objects’ attributes including an example of the overlapping ranges.

Table 6.2 Linguistic Variables Notations and Ranges Linguistic variable: objects’ attributes

Linguistic value Notation Numerical range

Low L [0.00 - 0.25]

Medium M [0.20 - 0.45]

The Thesaurus

The Thesaurus is used in this research in cases where the user searches for an objects and the entered name is not found in the database. The system will check the entered name in the thesaurus and will search the alternative word or sentence in our database having the same meaning. All image names matched in the thesaurus will be displayed providing they satisfy the other conditions like the supporting object names as their features, similar to that of a normal image search. Figure 6.1 illustrates the thesaurus cycle.

Not Found List of Images Query Processing Thesaurus Database Found