نتایج جستجو برای: shape retrieval
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With the growth of large image databases, content-based image retrieval systems are actually a highly challenging problem. The common approach is to extract a signature for every image based on different features (texture, color, shape analysis ) and to minimize a distance for retrieving similar images to a request one. Then, features extraction becomes the most important theme objectively , a ...
Shape-based Image Retrieval systems are currently one of the most active research areas in Content Based Multimedia Retrieval. However, as more methods are implemented, each with its unique characteristics and requirements, the creation and management of Image Retrieval Engines that utilize the best aspects of each shapematching method becomes even more difficult. Furthermore, it becomes almost...
Sketch-based 3D shape retrieval has become an important research topic in content-based 3D object retrieval. The aim of this track is to measure and compare the performance of sketch-based 3D shape retrieval methods implemented by different participants over the world. The track is based on a new sketch-based 3D shape benchmark, which contains two types of sketch queries and two versions of tar...
We aim at combining color and shape invariants for indexing and retrieving images. To this end, color models are proposed independent of the object geometry, object pose, and illumination. From these color models, color invariant edges are derived from which shape invariant features are computed. Computational methods are described to combine the color and shape invariants into a unified high-d...
As the most pervasive method of individual identification and document authentication, signatures present convincing evidence and provide an important form of indexing for effective document image processing and retrieval in a broad range of applications. In this work, we developed a fully automatic signature-based document image retrieval system that handles: 1) Automatic detection and segment...
Purpose: This paper presents various image indexing techniques and discusses their advantages and limitations. Methodology: conducting a review of the literature review, it identifies three main image indexing techniques, namely concept-based image indexing, content-based image indexing and folksonomy. It then describes each technique. Findings: Concept-based image indexing is te...
SHREC’10 robust large-scale shape retrieval benchmark simulates a retrieval scenario, in which the queries include multiple modifications and transformations of the same shape. The benchmark allows evaluating how algorithms cope with certain classes of transformations and what is the strength of the transformations that can be dealt with. The present paper is a report of the SHREC’10 robust lar...
Shape-based image and video retrieval is an active research topic in multimedia information retrieval. It is well known that there are significant variations in shapes of the same category extracted from images and videos. In this paper, we propose to use circular hidden Markov models for shape recognition and image retrieval. In our approach, we use a garbage state to explicitly deal with shap...
We present a new benchmark for testing algorithms that create canonical forms for use in non-rigid 3D shape retrieval. We have combined two existing datasets to create a varied collection of models for testing. Canonical forms attempt to factor out a shape’s pose, giving a pose-neutral shape. This opens up the possibility of using methods originally designed for rigid retrieval for the task of ...
Non-rigid 3D shape retrieval has become an important research direction in content-based 3D object retrieval. The aim of this track is to measure and compare the performance of non-rigid 3D shape retrieval methods implemented by different participants around the world. The track is based on a new non-rigid 3D shape benchmark, which contains 600 watertight triangle meshes that are equally classi...
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