Safa, HamrerasSmaine ,Mazouzi2024-03-262024-03-262021-04-04http://dspace.univ-skikda.dz:4000/handle/123456789/577The significant growth of images repositories in terms of content and usage has increased the need of conceiving powerful image retrieval systems. In this regard, CBIR is the process of searching images given a query based on their visual aspects. One of the most serious challenges that is facing CBIR systems is “the semantic gap”. This gap occurs between images representation given by a CBIR system and the true semantics included within these images. The semantic gap should be bridged or at least reduced in order to bring closer the low level vision of the CBIR system and the high level vision of the Human Visual System (HVS). This could be achieved through designing CBIR systems that behave with respect to the human understanding of images. In this thesis, we address this challenge by shedding light on a paradigm that has already imposed itself in many fields, that is the adaptation paradigm. Indeed, there is a need to inject a flexibility in the designed CBIR systems, so that they behave depending on some criterion, mainly the input data. We particularly integrate the adaptation into images characterization phase so as to reduce the semantic gap. This adaptation is carried out by selecting the characterization algorithm or tuning its parameters in a training phase, in order to increase its performance with respect to the target task. First, we start with the selection paradigm, where we apply the algorithm selection based on RICE model to select the best performing CBIR-algorithm for each image. In this work, the CBIR-algorithm was decomposed to many variants, where the feature variant was selected based on the input image. In the second contribution, we propose to use a Convolutional Neural Network (CNN) in the feature extraction stage. This latter is first finetuned on the target images dataset in order to adapt its parameters. The resulting network is then used to extract the class probability vectors from input images to use them as representations. Last, our main contribution consists of using an ensemble of CNNs that collaborate to extract relevant features from images. Herein, the class probability vectors are extracted from images using each ensemble member. They are then combined to make a powerful image representation. Finally, it is noteworthy that all our contributions have led to a significant improvement in the performance of the developed CBIR systems. This latter is a relevant indicator of narrowing the semantic gap within these systems, which is the ultimate goal of this thesisenThesis