This work presents a novel approach to content-based image retrieval in categorical multimedia databases. The images are indexed using a combination of text and content descriptors. The categories are viewed as semantic clusters of images and are used to confine the search space.

This is to certify that I have examined this copy of a master's thesis by Anand Sivaraman and have found that it is complete and satisfactory in all respects, and that any and all revisions required by the final examining committee have been made. ABSTRACT Content-based image retrieval systems retrieve images from a database that are

Texture is widely used in CBIR, and there have been a number of studies over the years to establish which features are perceptually significant. However, it is still diSJicult to retrieve reliably images that the human user would agree are “similar”. This paper reviews a range of computational methods, and compares their performance in class@ing and retrieving imagesporn the Brodatz set. Their ...

We describe the outcome of an effort to understand the behavior of content-based image retrieval (CBIR) technology by examining the behavior of a CBIR system in response to carefully constructed input query images. This work is preliminary and only considers a single CBIR system, SIMPLIcity[4]. We chose this particular system for expediency. It is our intention to develop a methodology suitable...

Kurzfassung. Taking an inspiration from psychological studies of visual attention, the contribution of this paper lies in prediction of the critical points of the trajectory using the structure of a scene and physical motion model. On one side, we present our approach for video analysis that differs from traditional tracking techniques by predicting future states of the moving object rather tha...

This document presents a State Of the Art related to most popular products, tools and methods related to CBIR.

To bridge the semantic gap in content-based image retrieval, detecting meaningful visual entities (e.g. faces, sky, foliage, buildings etc) in image content and classifying images into semantic categories based on trained pattern classifiers have become active research trends. In this paper, we present dual cascading learning frameworks that extract and combine intraimage and inter-class semant...

One of the challenges in image and video retrieval is the content-based retrieval of images and videos in the web. Less work has been done in this area, mainly due to scalability issues. For this reason, in this paper we investigate this problem by presenting tools for the characterization of the visual contents on specific web collections and a strategy for the search of faces in the web using...

This paper presents an application of perceptual grouping rules for content-based image retrieval. The semantic interrelationships between different primitive image features are exploited by perceptual grouping to detect the presence of manmade structures. A methodology based on these principles in a Bayesian framework for the retrieval of building images, and the results obtained are presented...

A perceptual approach to generating features for use in indexing and retrieving images is described. Salient regions that immediately attract the eye are colour (textured) regions that usually dominate an image. Features derived from these will allow search for images that are similar perceptually. We compute colour features and Gabor colour texture features on regions identified from a coarse ...