2 Interactive search from the user’s point of view
2.1 Query specification
2.2 Retrieval results
2.3 User interaction
2.4 The interface
2.5 Trends and advances
3 Interactive search from the system’s point of view
3.1 Image representation
3.2 Indexing and filtering
3.3 Active learning and classification
Cluster approaches: methods which represent the clusters of the images in feature space, such as query point or nearest neighbor-based learning.
Decision plane approaches: methods which represent the decision planes between clusters of images, such as artificial neural networks, support vector machines and kernel approaches.
Combining learners: methods that combine multiple classifiers to improve the overall accuracy.
Earth Mover’s distance
Unified feature matching
Integrated region matching
3.4 Similarity measures, distance and ranking
3.5 Long-term learning
3.6 Trends and advances
4 Evaluation and benchmarking
No. of images
University of Illinois at Urbana-Champaign
Massachusetts Institute of Technology
California Institute of Technology
PASCAL VOC 
ImageCLEF Medical 
Mean average precision
4.1 Image databases
4.2 Performance measures
4.3 Trends and advances
5 Discussion and conclusions
5.1 Promising research directions
Interaction in the question and answer paradigm The Q&A paradigm has the strength that it is probably the most natural and intuitive for the user. Recent Q&A research has focused significantly more on multimodal (as opposed to monomodal) approaches for both posing the questions and displaying the answers. These systems can also dynamically select the best types of media for clarifying the answer to a specific question.
Interaction on the learned models Beyond giving direct feedback on the results, preliminary work was started involving mid-level and high-level representations (see Sect. 3). Multi-scale approaches using segmented image components are certainly novel and promising.
Interaction by explanation: providing reasons along with results In the classic relevance feedback model, results are typically given but it is not clear to the user why the results were selected. In future interactive search systems, we expect to see systems which explain to the user why the results were chosen and allow the user to give feedback on the criteria used in the explanations, as opposed to only simply giving feedback on the image results.
Interaction with external or synthesized knowledge sources In the prior work in this area, most of the systems limited themselves only to the imagery in the local collection. However, it has been found that utilizing additional image collections and knowledge sources can significantly improve the quality of results. Currently, using very large multimedia databases such as Wikipedia as external knowledge sources is an active and fertile direction.
Social interaction: recommendation systems and collaborative filtering The small training set problem is of particular concern because humans do not want to label thousands of images. An interesting approach is to examine potential benefits from using algorithms from the area of collaborative filtering and recommendation systems. These systems have remarkably high performance in deciding which media items (often video) will be of interest to the user based on a social database of ranked items.