Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Multilinguistic handwritten character recognition by Bayesiandecision-based neural networks
IEEE Transactions on Signal Processing
Multiple instance learning via margin maximization
Applied Numerical Mathematics
A novel method for image retrieval using relevance feedback and unsupervised clustering
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
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In this paper, we propose a Generalized Probabilistic decision based Neural Network (GPDNN) for content-based image retrieval (CBIR). Instead of receiving the numerical values of each data points as the input, the proposed GPDNN models the I/O relationship via the distribution of input data and their corresponding outputs. The GPDNN involves the Multiple-Instance learning techniques to learn a desired concept. A set of exemplar images are selected by a user, each of which is labeled as conceptual related (positive) or conceptual unrelated (negative) image. Then, by using the proposed learning algorithm, an image classification system can learn the user's preferred image class from the positive and negative examples. The experimental results show that for only a few times of relearning, a user can use the prototype system to retrieve favor images from the database.