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

Live and Learn from Mistakes: A Lightweight System for Document Classification

We present a Life-Long Learning from Mistakes (3LM) algorithm for document classification, which could be used in various scenarios such as spam filtering, blog classification, web resource categorization, etc. We extend the ideas of online clustering and batch-mode centroid-based classification to online learning with negative feedback. The 3LM is a competitive learning algorithm, which avoids over-smoothing, characteristic of the centroid-based classifiers, by using a different class representative, which we call clusterhead. The clusterheads competing for vector-space dominance are drawn toward misclassified documents, eventually bringing the model to a "balanced state" for a fixed distribution of documents. Subsequently, the clusterheads oscillate between the misclassified documents, heuristically minimizing the rate of misclassifications, an NP-complete problem. Further, the 3LM algorithm prevents over-fitting by "leashing" the clusterheads to their respective centroids. A clusterhead provably converges if its class can be separated by a hyper-plane from all other classes. Lifelong learning with fixed learning rate allows 3LM to adapt to possibly changing distribution of the data and continually learn and unlearn document classes. We report on our experiments, which demonstrate high accuracy of document classification on Reuters21578, OHSUMED, and TREC07p-spam datasets. The 3LM algorithm did not show over-fitting, while consistently outperforming centroid-based, Naïve Bayes, C4.5, AdaBoost, kNN, and SVM whose accuracy had been reported on the same three corpora

Total Variability Space for LDA-based multi-viewtext categorization

Paru sous le titre Compact Multiview Representation of Documents Based on the Total Variability SpaceInternational audienceMapping text document into LDA-based topic-space is a classical way to extract high level representation of text documents. Unfortunatly , LDA is higly sensitive to hyper-parameters related to class number or word and topic distribution , and there is not any systematic way to prior estimate optimal configurations. Morover , various hyperparameter configurations offer complementary views on the document. In this paper , we propose a method based on a two-step process that , first , expands representation space by using a set of topic spaces and , second , compacts representation space by removing poorly relevant dimensions. These two steps are based respectivelly on multi-view LDA-based representation spaces and factor-analysis models. This model provides a view-independant representation of documents while extracting complementary information from a massive multi-view representation. Experiments are conducted on the DECODA conversation corpus and Reuters-21578 textual dataset. Results show the effectiveness of the proposed multi-view compact representation paradigm. The proposed categorization system reaches an accuracy of 86. 9% and 86. 5% respectively with manual and automatic transcriptions of conversations , and a macro-F1 of 80% during a classification task of the well-known studied Reuters-21578 corpus , with a significant gain compared to the baseline (best single topic space configuration) , as well as methods and document representations previously studied

On the use of Machine Learning and Deep Learning for Text Similarity and Categorization and its Application to Troubleshooting Automation

Troubleshooting is a labor-intensive task that includes repetitive solutions to similar problems. This task can be partially or fully automated using text-similarity matching to find previous solutions, lowering the workload of technicians. We develop a systematic literature review to identify the best approaches to solve the problem of troubleshooting automation and classify incidents effectively. We identify promising approaches and point in the direction of a comprehensive set of solutions that could be employed in solving the troubleshooting automation problem