The impact of the image processing in the indexation system

This paper presents an efficient word spotting system applied to handwritten Arabic documents, where images are represented with bag-of-visual-SIFT descriptors and a sliding window approach is used to locate the regions that are most similar to the query by following the query-by-example paragon. First, a pre-processing step is used to produce a better representation of the most informative features. Secondly, a region-based framework is deployed to represent each local region by a bag-of-visual-SIFT descriptors. Afterward, some experiments are in order to demonstrate the codebook size influence on the efficiency of the system, by analyzing the curse of dimensionality curve. In the end, to measure the similarity score, a floating distance based on the descriptor’s number for each query is adopted. The experimental results prove the efficiency of the proposed processing steps in the word spotting system.

An Advanced Method for Human Body Detection

An advanced method is proposed that combines the advantage of Hough transform and exemplar method for human detection. This method firstly uses a universal codebook for all the patches in an image which reduces the total codebook size and the dimensionality of similarity features. Another contribution is in classifying and detection, in which section we use the sprite of Hough transform, but we train the model in a boost way. Finally the experiment results are given.