In general, an image retrieval system usually provides a user interface for communicating with the user. It collects the required information, including the query image, from the user and displays the retrieval results to him. However, as the images are matched based on low-level visual features, the target or the similar images may be far away from the query in the feature space, and they are not returned in the limited number of retrieved images of the first display. Therefore, in some retrieval systems, there is a relevance feedback from the user, where human and computer can interact to increase retrieval performance.
According to the aforementioned concept, we design a user oriented image retrieval system based on IGA, as shown in Fig. 3.
Our system operates in four phases.
1) Querying: The user provides a sample image as the query for the system.
2) Similarity computation: The system computes the similarity between the query image and the database images according to the aforementioned low-level visual features.
3) Retrieval: The system retrieves and presents a sequence of images ranked in decreasing order of similarity. As a result, the user is able to find relevant images by getting the top-ranked images first.
4) Incremental search: After obtaining some relevant images, the system provides an interactive mechanism via IGA, which lets the user evaluates the retrieved images as more or less relevant to the query one, and the system then updates the relevance information to include as many user-desired images as possible in the next retrieval result.
When we apply the IGA to develop a content-based color image retrieval system, we must consider the following components:
1) A genetic representation of solutions to the problem;
2) One way to create the initial population of solutions;
3) An evaluation function that rates all candidate solutions according to their “fitness”; and
4) Genetic operators that alter genetic composition of children during reproduction.
Solution representation: In order to apply GA to a given problem, one has to make a decision to find an appropriates genotype that the problem needs, i.e., the chromosome representation. In the proposed approach, a chromosome represents the considered three types of image features (i.e., color, texture, and edge) in an image.
Initial population: The IGA requires a population of potential solutions to be initialized at the beginning of the GA process. Usually, the initialization process varies with the applications; here, we adopt the first query results of a sample image as initial candidate images.
Fitness function: The fitness function is employed to evaluate the quality of the chromosomes in the population. The use of IGA allows the fusion of human and computer efforts for problem solving . Since the objective of our system is to retrieve the images that are most satisfied to the users’ need, the evaluation might simultaneously incorporate users’ subjective evaluation and intrinsic characteristics of the images. Hence, in our approach, the quality of a chromosome C with relation to the query q is defined as
where sim(q,C) represents the similarity measure between images, d indicates the impact factor of human’s judgment, the coefficients w1 and w2 determine the relative importance of them to calculate the fitness, and £wi = 1. In this paper, they are both set to 0.5. The similarity measure between images is defined as
where represent the normalized mean value and standard deviation of the image I in t color space, respectively. BMI means the image bitmap feature of the image I; meanwhile, EI and EHDI represent the entropy
A user’s preference is included in the fitness evaluated by the user.We use an impact factor to indicate the human’s judgment or preferences, and the values of the impact factor are carried out with constant range from 0.0 to 1.0 with an interval of 0.1. and the EHD of the image I, respectively. For two images, the hamming distance used to evaluate the image bitmap similarity is defined by Genetic operators: The selection operator determines which chromosomes are chosen for mating and how many off- spring that each selected chromosome produces. Here, we adopt the tournament selection method because the time complexity of it is low. It does not require a global fitness comparison of all individuals in a population; therefore, it can accelerate the evolution process. The crossover operator randomly pairs chromosomes and swaps parts of their genetic information to produce new chromosomes.
We use the one-point crossover in the proposed approach. Parts of the two chromosomes selected based on fitness are swapped to generate trait-preserving offsprings. The mutation operator creates a new chromosome in order to increase the variability of the population. However, in order to speed up the evaluation process, we do not consider the mutation operator.