As digital images become ubiquitous in many applications, the need for efficient and effective retrieval techniques is more demanding than ever. Query by Example (QBE) and Query by Concept (QBC) are among the most popular query models. The former model accepts example images as queries and searches for similar ones based on low-level features such as colors and textures. The latter model allows queries to be expressed in the form of high-level semantics or concept words, such as "boat" or "car," and finds images that match the specified concepts. Recent research has focused on the connections between these two models and attempts to close the semantic-gap between them. This research involves finding the best method that maps a set of low-level features into high-level concepts. Automatic annotation techniques are investigated in this dissertation to facilitate QBC. In this approach, sets of training images are used to discover the relationship between low-level features and predetermined high-level concepts. The best mapping with respect to the training sets is proposed and used to analyze images, annotating them with the matched concept words. One principal difference between QBE and QBC is that, while similarity matching in QBE must be done at the query time, QBC performs concept exploration off-line. This difference allows QBC techniques to shift the time-consuming task of determining similarity away from the query time, thus facilitating the additional processing time required for increasingly accurate matching. Consequently, QBC's primary design objective is to achieve accurate annotation within a reasonable processing time. This objective is the guiding principle in the design of the following proposed methods which facilitate image annotation: 1.A novel dynamic similarity function. This technique allows users to query with multiple examples: relevant, irrelevant or neutral. It uses the range distance in each group to automatically determine weights in the distance function. Among the advantages of this technique are higher precision and recall rates with fast matching time. 2.Object recognition based on skeletal graphs. The topologies of objects' skeletal graphs are captured and compared at the node level. Such graph representation allows preservation of the skeletal graph's coherence without sacrificing the flexibility of matching similar portions of graphs across different levels. The technique is robust to translation, scaling, and rotation invariants at object level. This technique achieves high precision and recall rates with reasonable matching time and storage space. 3.ASIA (Automatic Sampling-based Image Annotation) is a technique based on a new sampling-based matching framework allowing users to identify their area of interest. ASIA eliminates noise, or irrelevant areas of the image. ASIA is robust to translation, scaling, and rotation invariants at the object level. This technique also achieves high precision and recall rates. While the above techniques may not be the fastest when contrasted with some other recent QBE techniques, they very effectively perform image annotation. The results of applying these processes are accurately annotated database images to which QBC may then be applied. The results of extensive experiments are presented to substantiate the performance advantages of the proposed techniques and allow them to be compared with other recent high-performance techniques. Additionally, a discussion on merging the proposed techniques into a highly effective annotation system is also detailed.