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We propose a new segmentation-free method for keyword spotting in handwritten documents based on Heat Kernel Signature (HKS). After key points are located by the key point detector for SIFT on the document pages and the query image, HKS descriptors are extracted from a local patch centered at each key point. In order
associated with an image. In our approach, we divide images into small tiles and create visual keywords using a high-dimensional clustering algorithm. These visual keywords act the same as text keywords. One of the challenges of this approach is to identify an appropriate size for visual keywords. In this paper, we report our
The Fisher kernel is a generic framework which combines the benefits of generative and discriminative approaches to pattern classification. In this contribution, we propose to apply this framework to handwritten word-spotting. Given a word image and a keyword generative model, the idea is to generate a vector which
Image annotation is a challenging task that allows to correlate text keywords with an image. In this paper we address the problem of image annotation using Kernel Multiple Linear Regression model. Multiple Linear Regression (MLR) model reconstructs image caption from an image by performing a linear transformation of
A multi-agent based Web mining model is designed for the improvement of the efficiency of keywords based search engine. The model divides mining task into several parallel agents which coordinately work together, and the mining efficiency is improved greatly. Evolving from HITS, algorithm named Grabber in the model
In text categorization, vectorizing a document by probability distribution is an effective dimension reduction way to save training time. However, the data sets that share many common keywords between categories affect the classification performance seriously. To address that problem, firstly, we conduct an effective
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