## 3 Class Imbalance Problem
### Genetic Algorithm Architecture for Class Imbalance Problem
#### 3.4.2 Genetic Operators
For stable predictors, this strategy may not work. However, for unstable predictors performance improvement occurs. Neural networks and SVM are unstable for asymmetric bagging and symmetric bagging, therefore strategy improves the retrieval performance. ### Content Based Image Retrieval

Once the classifiers are trained, the semantic association of all the images present in the image repository is determined. The semantic association values are stored in a file that serves as the semantic association database. Therefore, when system suggests the semantic class for any query image, top \(k\) images having the same semantic class are returned to the user after ranking on the basis of distance as the image retrieval output. For some image queries, the system may not be able to determine the right semantic class--the returned output is comprised of the undesired images. To avoid this situation, the system also returns the query top neighbours to the user, and enable relevance feedback over them. Relevance feedback gives freedom to the user to guide the image retrieval system in the case of complex queries to achievethe desired output. Relevance feedback is covered in detail in the experimentation section. Both sets of images are returned to the user in the form of representative images. These representative images are selected from both output sets, and are the images that appear most similar to the query image in terms of distance. ## 5 Experiment and Results

To evaluate the proposed method, an extensive set of experiments are conducted and performance is evaluated by comparing the proposed algorithm against several state-of-the-art CBIR methods. Details of the experiments and comparative analysis are presented in the following subsections. ### Database Description

To elaborate the effectiveness of the proposed method in terms of content based image retrieval, extensive experiments are conducted on the Corel dataset with 10,908 images. The Corel dataset has two versions, Corel set A and Corel set B. Corel set A has 1000 images grouped in 10 semantic categories namely: Africa, beach, buildings, buses, dinosaurs, elephants, flowers, horses, mountains and food (Figure 3) [41]. Corel set B has 9908 images belonging from several groups like sunset, texture, butterfly, birds, animals, jungle, cars and boats [4]. For implementation of this work, both versions of the Corel datasets are combined, but the results are reported only on the Corel set A, as it is a standard approach followed in several state of the art CBIR systems [4,8,41,42]. Hence, a clear performance comparison is possible. To further elaborate the retrieval performance, the Columbia object image library (COIL) [43] and Caltech-101 [44] datasets are used. The COIL dataset has 7200 images from 100 different semantic groups, whereas the Caltech 101 image set is comprised of 101 different semantic categories. ### Precision and Recall Evaluation

To evaluate the performance of the proposed method, we determine how many relevant images are obtained in response of image queries. For performance evaluation precision and recall rates are used. Precision determines the ability of system to retrieve only those images that are relevant for image queries in the retrieved output and can be defined as:

\[Precision=\frac{No.\ of\ relevant\ images\ retrieved}{Total\ number\ of\ images\ retrieved}.\]

Recall rate determines the ability of the classifier system in terms of model association with their actual class [27] and can be defined as:

\[Recall=\frac{No.\ of\ relevant\ images\ retrieved}{Total\ number\ of\ relevant\ images}.\]

Figure 3: Sample images of each category of Corel set A. In our implementation, the results are reported by running a computer simulation for five times that randomly selects twenty image queries from each image category and the precision and recall rates are reported on the top 20 images. In order to show the superiority of the proposed technique, the results are also compared with those of [PERSON] et al. [45], [PERSON] et al. [46], [PERSON] et al.