Generalizing Operations of Binary Autoassociative Morphological Memories Using Fuzzy Set Theory
Journal of Mathematical Imaging and Vision
Reconstruction of Patterns from Noisy Inputs Using Morphological Associative Memories
Journal of Mathematical Imaging and Vision
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
IEEE Transactions on Computers
Associative Memories Applied to Pattern Recognition
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
A New Associative Model with Dynamical Synapses
Neural Processing Letters
3D object recognition based on low frequency response and random feature selection
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Face recognition using some aspects of the infant vision system and associative memories
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
A new bi-directional associative memory
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Median hetero-associative memories applied to the categorization of true-color patterns
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
FPGA-Based architecture for extended associative memories and its application in image recognition
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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In this paper we describe how associative memories can be applied to categorize images. If we present to an associative memory (AM) an image we would expect that the AM would respond with something that describes the content of the image; for example, if the image contains a tiger we would expect that the AM would respond with the word “tiger”. In order to achieve this goal, we first chose a set of images. Each image is next associated to the word that better describes the content of the image. With this information an AM is trained as in [10]. We then use the AM to categorize instances of images with the same content even if these images are distorted by some kind of noise. The accuracy of the proposal is tested using a set of images containing different species of flowers and animals.