Arabic Text Classification Framework Based on Latent Dirichlet Allocation

In this paper, we present a new algorithm based on the LDA (Latent Dirichlet Allocation) and the Support Vector Machine (SVM) used in the classification of Arabic texts.Current research usually adopts Vector Space Model to represent documents in Text Classification applications. In this way, document is coded as a vector of words; n-grams. These features cannot indicate semantic or textual content; it results in huge feature space and semantic loss. The proposed model in this work adopts a “topics” sampled by LDA model as text features. It effectively avoids the above problems. We extracted significant themes (topics) of all texts, each theme is described by a particular distribution of descriptors, then each text is represented on the vectors of these topics. Experiments are conducted using an in-house corpus of Arabic texts. Precision, recall and F-measure are used to quantify categorization effectiveness. The results show that the proposed LDA-SVM algorithm is able to achieve high effectiveness for Arabic text classification task (Macro-averaged F1 88.1% and Micro-averaged F1 91.4%)

A Review of Codebook Models in Patch-Based Visual Object Recognition

The codebook model-based approach, while ignoring any structural aspect in vision, nonetheless provides state-of-the-art performances on current datasets. The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of such a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. In our recent work, we proposed a resource-allocating codebook, to constructing a discriminant codebook in a one-pass design procedure that slightly outperforms more traditional approaches at drastically reduced computing times. In this review we survey several approaches that have been proposed over the last decade with their use of feature detectors, descriptors, codebook construction schemes, choice of classifiers in recognising objects, and datasets that were used in evaluating the proposed methods