Image Retrieval with Relational Semantic Indexing Color and Gray Images

Due to the development of digital technology large number of image is available in web and personal database and it take more time to classify and organize them. In AIA assigns label to image content with this image is automatically classified and desired image can be retrieved. Image retrieval is the one of the growing research area. To retrieve image Text and content based methods used. In recent research focus on annotation based retrieval. Image annotation represents assigning keywords to image based on its contents and it use machine learning techniques. Using image content with more relevant keywords leads fast indexing and retrieval of image from large collection of image database. Many techniques have been proposed for the last decades and it gives some improvement in retrieval performance. In this proposed work Relational Semantic Indexing (RSI) based LQT technique reduces the search time and increase the retrieval performance. This proposed method includes segmentation, feature extraction, classification, and RSI based annotation steps. This proposed method compared against IAIA, and LSH algorithm

Image Annotation and Topic Extraction Using Super-Word Latent Dirichlet

This research presents a multi-domain solution that uses text and images to iteratively improve automated information extraction. Stage I uses local text surrounding an embedded image to provide clues that help rank-order possible image annotations. These annotations are forwarded to Stage II, where the image annotations from Stage I are used as highly-relevant super-words to improve extraction of topics. The model probabilities from the super-words in Stage II are forwarded to Stage III where they are used to refine the automated image annotation developed in Stage I. All stages demonstrate improvement over existing equivalent algorithms in the literature

Blending Learning and Inference in Structured Prediction

In this paper we derive an efficient algorithm to learn the parameters of structured predictors in general graphical models. This algorithm blends the learning and inference tasks, which results in a significant speedup over traditional approaches, such as conditional random fields and structured support vector machines. For this purpose we utilize the structures of the predictors to describe a low dimensional structured prediction task which encourages local consistencies within the different structures while learning the parameters of the model. Convexity of the learning task provides the means to enforce the consistencies between the different parts. The inference-learning blending algorithm that we propose is guaranteed to converge to the optimum of the low dimensional primal and dual programs. Unlike many of the existing approaches, the inference-learning blending allows us to learn efficiently high-order graphical models, over regions of any size, and very large number of parameters. We demonstrate the effectiveness of our approach, while presenting state-of-the-art results in stereo estimation, semantic segmentation, shape reconstruction, and indoor scene understanding