An image content adaptation for visually impaired people based on the MPEG-21 Digital Item Adaptation (DIA)
standard is proposed. The content adaptation mainly considers spatial contrast vision characteristic of users, which is
represented by a contrast sensitivity function (CSF). There are three key contributions of the paper. First, the visual
perception of users who have different spatial contrast vision abilities is simulated by incorporating the HVS model
proposed by Pattanaik et al. Second, to measure spatial contrast vision, and thus realizing personalized content
adaptation depending on the severity of the visual ability of individual user, CSF is measured on computer-based
environment. The measured spatial contrast vision symptom and its severity, is represented in an interoperable way by
using an example of extended description tool provided by the MPEG-21 DIA specification. Third, the content
adaption is also proposed, which is personalized in a sense that the adapted content would be optimized to the given
description of a particular symptom and its severity. To assess the effectiveness of the proposed methods, we performed
a number of experiments targeting users with a low vision and showed how to determine and describe the CSF
parameters. Furthermore, statistical experiment is performed to verify the effectiveness of the proposed adaptation
process for users with the low vision symptom.
In this paper, we propose a region-based image retrieval system using EHD (Edge Histogram Descriptor) and CLD (Color Layout Descriptor) of MPEG-7 descriptors. The combined descriptor can efficiently describe edge and color features in terms of sub-image regions. That is, the basic unit for the selection of the region-of-interest (ROI) in the image is the sub-image block of the EHD, which corresponds to 16 (i.e., 4x4) non-overlapping image blocks in the image space. This implies that, to have a one-to-one region correspondence between EHD and CLD, we need to take an 8x8 inverse DCT (IDCT) for the CLD. Experimental results show that the proposed retrieval scheme can be used for image retrieval with the ROI based image retrieval for MPEG-7 indexed images.
In this paper, we design and implement a concept-based image retrieval system using feature information, more specifically,
edge histogram description. The general edge histogram framework is a novel index mechanism which allows us to describe a content of images. However, there is a significant drawback in the framework that it can not accommodate a concept-based retrieval. Even if images are only conceptually related with user queries, it may be capable of proving them to be irrelevant since their features can be different each other. Our system adapts an edge histogram descriptor and includes a knowledge used for capturing concepts from images. In the knowledge base, a concept is expressed as some of templates, which can be described by common edge histograms for the images to represent the concept well. The templates can be generated by
clustering the training images related with a concept. Consequently, since an image can also be matched with some of the templates, our system is able to support an automatic mechanism for indexing the image with the concept. The indexing mechanism enables users to retrieve the images related with a query which is formulated with their intended concepts. In addition, we also demonstrate that our concept-based approach makes a favorable comparison with an approach based on color or edge features.