LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
IEEE Transactions on Pattern Analysis and Machine Intelligence
A nearest-neighbor approach to relevance feedback in content based image retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Real-Time Computerized Annotation of Pictures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lire: lucene image retrieval: an extensible java CBIR library
MM '08 Proceedings of the 16th ACM international conference on Multimedia
IEEE Transactions on Knowledge and Data Engineering
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
CEDD: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
IEEE Transactions on Multimedia
k-nearest neighbors directed noise injection in multilayer perceptron training
IEEE Transactions on Neural Networks
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Nowadays very large archives of digital images are easily produced thanks to the wide availability of digital cameras, that are often embedded into a number of portable devices. One of the ways of exploring an image archive is to search for similar images. Relevance feedback mechanisms can be employed to refine the search, as the most similar images according to a set of visual features may not contain the same semantic concepts according to the users' needs. Relevance feedback allows users to label the images returned by the system as being relevant or not. Then, this labelled set is used to learn the characteristics of relevant images. As the number of images provided to users to receive feedback is usually quite small, and relevant images typically represent a tiny fraction, it turns out that the learning problem is heavily imbalanced. In order to reduce this imbalance, this paper proposes the use of techniques aimed at artificially increasing the number of examples of the relevant class. The new examples are generated as new points in the feature space so that they are in agreement with the local distribution of the available relevant examples. The locality of the proposed approach makes it quite suited to relevance feedback techniques based on the Nearest-Neighbor (NN) paradigm. The effectiveness of the proposed approach is assessed on two image datasets and comparisons with editing techniques that eliminate redundancies in non-relevant examples are also reported.