###### Abstract  
In order to lower the dependence on textual annotations for image searches, the content based image retrieval (CBIR) has become a popular topic in computer vision. A wide range of CBIR applications consider classification techniques, such as artificial neural networks (ANN), support vector machines (SVM), etc. to understand the query image content to retrieve relevant output. However, in multi-class search environments, the retrieval results are far from optimal due to overlapping semantics amongst subjects of various classes. The classification through multiple classifiers generate better results, but as the number of negative examples increases due to highly correlated semantic classes, classification bias occurs towards the negative class, hence, the combination of the classifiers become even more unstable particularly in one-against-all classification scenarios. In order to resolve this issue, a genetic algorithm (GA) based classifier comity learning (GCCL) method is presented in this paper to generate stable classifiers by combining ANN with SVMs through asymmetric and symmetric bagging. The proposed approach resolves the classification disagreement amongst different classifiers and also resolves the class imbalance problem in CBIR. Once the stable classifiers are generated, the query image is presented to the trained model to understand the underlying semantic content of the query image for association with the precise semantic class. Afterwards, the feature similarity is computed within the obtained class to generate the semantic response of the system. The experiments reveal that the proposed method outperforms various state-of-the-art methods and significantly improves the image retrieval performance. CBIR; genetic algorithms; SVM; neural networks; semantic association; asymmetric bagging 2018  
applications in many other fields, including architectural design [1], geographical information systems [2], surveillance systems [3], remote sensing [2], etc. [4; 5; 6]. CBIR systems consider visual attributes of images, such as colour, shape, texture and salient image points, to build feature repository. Closely resembled images, in terms of the feature distance, are returned as the semantic response of a CBIR system against queries acquired in the form of images. However, due to the inherent gap between high level semantics prevailing in images and low level feature representations, the image retrieval performance of CBIR systems is poor. In order to enhance image retrieval performance, many researchers [7; 8; 9] have used machine learning algorithms to understand image semantics through feature representations and gained remarkable results [10]. [PERSON] et al. applied multi-class support vector machines (SVM) on the feature repository obtained by the Bandlet transform [8]. [PERSON] et al. achieved the structural granularity through the combination of structured large margin machine (SLMM) and the Laplacian-SVM (LapSVM) for image retrieval [11]. [PERSON] et al. applied a graph based clustering method to obtain a semantic association of image queries [12]. [PERSON] et al. applied a multi-tasking support machine for optimal feature selection and image retrieval [13]. No matter how image classification occurs, classifier learning becomes a real challenge when training sets are severely imbalanced (i.e., the class of interest is under represented in the training set) particularly in the presence of overlapping semantic classes with a small sample size [14]. The evaluation criterion that guides the learning procedure leads to ignoring minority class examples, and resultantly the induced classifier loses its classification ability. As a usual example, consider a bi-class dataset with imbalance ratio of 1:100; a classifier that tries to maximize the accuracy of classification may end up with an accuracy of 90% by only discounting the positive examples. The chances of incorrect association further increase when the training set comprises of multiple highly correlated classes, as is the case in CBIR.