Constraint-Based Clustering of Image Search Results Using Photo Metadata and Low-Level Image Features

2010 ◽  
pp. 165-178 ◽  
Author(s):  
Masaharu Hirota ◽  
Shohei Yokoyama ◽  
Naoki Fukuta ◽  
Hiroshi Ishikawa
Keyword(s):  
Image Features ◽  
Image Search ◽  
Low Level ◽  
Search Results

Context-Aware Medical Image Retrieval for Improved Dementia Diagnosis

2013 ◽  
pp. 434-448 ◽  
Author(s):  
Melih Soydemir ◽  
Devrim Unay
Keyword(s):  
Image Features ◽  
Added Value ◽  
Image Search ◽  
Contextual Knowledge ◽  
Dementia Diagnosis ◽  
Search Results ◽  
Diagnostic Decision ◽  
Increasing Demand ◽  
Multiple Patients ◽  
Content Information

Progress in medical imaging technology together with the increasing demand for confirming a diagnostic decision with objective, repeatable, and reliable measures for improved healthcare have multiplied the number of digital medical images that need to be processed, stored, managed, and searched. Comparison of multiple patients, their pathologies, and progresses by using image search systems may largely contribute to improved diagnosis and education of medical students and residents. Supporting image content information with contextual knowledge will lead to increased reliability, robustness, and accuracy in search results. To this end, the authors present an image search system that permits search by a multitude of image features (content), and demographics, patient’s medical history, clinical data, and ontologies (context). Moreover, they validate the system’s added value in dementia diagnosis via evaluations on publicly available image databases.

Machine Learning Approach for Content Based Image Retrieval

2012 ◽  
pp. 1-11 ◽  
Author(s):  
Siddhivinayak Kulkarni
Keyword(s):  
Neural Networks ◽  
Image Retrieval ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
The Internet ◽  
Learning Approach ◽  
Query Image ◽  
Low Level ◽  
Machine Learning Approach ◽  
Efficient Retrieval

Developments in technology and the Internet have led to an increase in number of digital images and videos. Thousands of images are added to WWW every day. Content based Image Retrieval (CBIR) system typically consists of a query example image, given by the user as an input, from which low-level image features are extracted. These low level image features are used to find images in the database which are most similar to the query image and ranked according their similarity. This chapter evaluates various CBIR techniques based on fuzzy logic and neural networks and proposes a novel fuzzy approach to classify the colour images based on their content, to pose a query in terms of natural language and fuse the queries based on neural networks for fast and efficient retrieval. A number of experiments were conducted for classification, and retrieval of images on sets of images and promising results were obtained.

Toward Semantically Meaningful Feature Spaces for Efficient Indexing in Large Image Databases

2011 ◽  
pp. 1-29
Author(s):  
Anne H.H. Ngu ◽  
Jialie Shen ◽  
John Shepherd
Keyword(s):  
Distance Function ◽  
Image Features ◽  
Image Databases ◽  
Multimedia Databases ◽  
Visual Features ◽  
Hybrid Architecture ◽  
Low Level ◽  
Feature Vectors ◽  
Training Cost ◽  
Low Dimensional

The optimized distance-based access methods currently available for multimedia databases are based on two major assumptions: a suitable distance function is known a priori, and the dimensionality of image features is low. The standard approach to building image databases is to represent images via vectors based on low-level visual features and make retrieval based on these vectors. However, due to the large gap between the semantic notions and low-level visual content, it is extremely difficult to define a distance function that accurately captures the similarity of images as perceived by humans. Furthermore, popular dimension reduction methods suffer from either the inability to capture the nonlinear correlations among raw data or very expensive training cost. To address the problems, in this chapter we introduce a new indexing technique called Combining Multiple Visual Features (CMVF) that integrates multiple visual features to get better query effectiveness. Our approach is able to produce low-dimensional image feature vectors that include not only low-level visual properties but also high-level semantic properties. The hybrid architecture can produce feature vectors that capture the salient properties of images yet are small enough to allow the use of existing high-dimensional indexing methods to provide efficient and effective retrieval.

Visual diversification of image search results

2009 ◽  
Author(s):  
Reinier H. van Leuken ◽  
Lluis Garcia ◽  
Ximena Olivares ◽  
Roelof van Zwol
Keyword(s):  
Image Search ◽  
Search Results

A Method of Image Affective Semantic Classification Based on Attribution Reduction

2014 ◽  
Vol 556-562 ◽  
pp. 4820-4824
Author(s):  
Ying Xia ◽  
Le Mi ◽  
Hae Young Bae
Keyword(s):  
Feature Selection ◽  
Rough Set ◽  
Classification Accuracy ◽  
Selection Process ◽  
Attribute Reduction ◽  
Image Features ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Semantic Classification ◽  
Low Level

In study of image affective semantic classification, one problem is the low classification accuracy caused by low-level redundant features. To eliminate the redundancy, a novel image affective classification method based on attributes reduction is proposed. In this method, a decision table is built from the extraction of image features first. And then valid low-level features are determined through the feature selection process using the rough set attribute reduction algorithm. Finally, the semantic recognition is done using SVM. Experiment results show that the proposed method improves the accuracy in image affective semantic classification significantly.

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