Image Retrieval Based on Multi Structure Co-occurrence Descriptor

This study present a new technique for Batik cloth image retrieval using Micro-Structure Co-occurence Descriptor (MSCD). MSCD is a developed method based on Enhanced Micro Structure Descriptor (EMSD). Previously, EMSD has been improved by adding edge orientation feature. In previous study, EMSD cannot achieve an optimal precision. Therefore, MSCD is proposed to overcome the EMSD drawback using global feature approach, namely Gray Level Co-occurrence Matrix (GLCM). There are 300 batik cloth images which contain 50 classes used for dataset. The performance result show that MSCD can retrieve Batik cloth images more effective than EMSD

An Enhanced Image retrieval Technique based on Edge-Orientation Technique

With the tremendous development in Networking and Multimedia technologies, Image Retrieval plays significant roleand is used for browsing, searching and retrieving images from a large database of digital images. Image Retrieval techniques utilize annotation methods of adding metadata such as captioning, keywords or descriptions to the images. The manual image annotation is much time consuming laborious and expensive.As the data bases size increases, annotation becomes a tedious task. Thus automatic image annotationhas drawn the attention of the researchers in recent years.. The increase in social web application and the semantic web drawn attention of researchers in  the development of several web-based image retrieval tools. This paper presents an easy, efficient image retrieval approach using a new image feature descriptor called Micro-Structure Descriptor (MSD). The microstructures are defined based on an edge orientation similarly while the MSD is built based on the underlying colors in micro-structures with similar edge orientation.In this method of Image retrieval the MSD extracts features by simulating human’s early visual processing by effectively integrating color, texture, color lay out information and shape. The proposed MSD algorithm has high indexing performance and low dimensionalityas it has only 72 dimensions for full color images.The technique is examined on Corel datasets with natural images; the results demonstrate that this image retrieval method is much more efficient and effective than reprehensive feature descriptors, such as Gabor features and Multi Texton Histograms

High-Precision Localization Using Ground Texture

Location-aware applications play an increasingly critical role in everyday life. However, satellite-based localization (e.g., GPS) has limited accuracy and can be unusable in dense urban areas and indoors. We introduce an image-based global localization system that is accurate to a few millimeters and performs reliable localization both indoors and outside. The key idea is to capture and index distinctive local keypoints in ground textures. This is based on the observation that ground textures including wood, carpet, tile, concrete, and asphalt may look random and homogeneous, but all contain cracks, scratches, or unique arrangements of fibers. These imperfections are persistent, and can serve as local features. Our system incorporates a downward-facing camera to capture the fine texture of the ground, together with an image processing pipeline that locates the captured texture patch in a compact database constructed offline. We demonstrate the capability of our system to robustly, accurately, and quickly locate test images on various types of outdoor and indoor ground surfaces