Harvesting Image Databases from The Web

The research work presented here includes data mining needs and study of their algorithm for various extraction purpose. It also includes work that has been done in the field of harvesting images from web. Here the proposed method is to harvest image databases from web. We can automatically generate a large number of images for a specified object. By applying concept of data mining and the algorithm from data mining which is used for extraction of data or harvesting images. A multimodal approach employing text ,metadata and visual  features is used to gather many high-quality images from the web. The modules can be made to find query images by selecting images where nearby text is top ranked by the topic i.e., formation of image clusters then download associate images by using approaches like web search, image search and Google images. Apply re-ranking algorithm and then filtering process to harvest the images.Currently, image search gives a very low precision (only about 4%) and is not used for the harvesting experiments. Since the movements of the technologies are growing rapidly the kinds of work also need to be grown up. This work shows an approach to harvest a large number of images of a particular class automatically and to achieve this with high precision by providing training databases so that a new object model can be learned effortlessly. Many other tools also are available for harvesting images from web .An approach in this paper is original and up to the mark. Keywords: Legacy code, re-engineering, class diagrams, Aggregation, Association, Attribute

The Libra Toolkit for Probabilistic Models

The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, and sum-product networks. Compared to other toolkits, Libra places a greater emphasis on learning the structure of tractable models in which exact inference is efficient. It also includes a variety of algorithms for learning graphical models in which inference is potentially intractable, and for performing exact and approximate inference. Libra is released under a 2-clause BSD license to encourage broad use in academia and industry

Beyond Physical Connections: Tree Models in Human Pose Estimation

Simple tree models for articulated objects prevails in the last decade. However, it is also believed that these simple tree models are not capable of capturing large variations in many scenarios, such as human pose estimation. This paper attempts to address three questions: 1) are simple tree models sufficient? more specifically, 2) how to use tree models effectively in human pose estimation? and 3) how shall we use combined parts together with single parts efficiently? Assuming we have a set of single parts and combined parts, and the goal is to estimate a joint distribution of their locations. We surprisingly find that no latent variables are introduced in the Leeds Sport Dataset (LSP) during learning latent trees for deformable model, which aims at approximating the joint distributions of body part locations using minimal tree structure. This suggests one can straightforwardly use a mixed representation of single and combined parts to approximate their joint distribution in a simple tree model. As such, one only needs to build Visual Categories of the combined parts, and then perform inference on the learned latent tree. Our method outperformed the state of the art on the LSP, both in the scenarios when the training images are from the same dataset and from the PARSE dataset. Experiments on animal images from the VOC challenge further support our findings.Comment: CVPR 201