Image-to-word transformation based on dividing and vector quantizing images with words
Abstract:
We propose a method to make a relationship between images and words. We adopt two processes in the method, one is a process to uniformly divide each image into sub-images with key words, and the other is a process to carry out vector quantization of the sub-images. These processes lead to results which show that each sub-image can be correlated to a set of words each of which is selected from words assigned to whole images. Original aspec ....etc

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ABSTRACT
For languages without word boundary delimiters, dictionaries are
needed for segmenting running texts. This figure makes
segmentation accuracy depend significantly on the quality of the
dictionary used for analysis. If the dictionary is not sufficiently
good, it will lead to a great number of unknown or unrecognized
words. These unrecognized words certainly reduce segmentation
accuracy. To solve such problem, we propose a method based on
decision tree models. Without use of a dictionary, specific
information, called syn ....etc

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