A content-based image retrieval scheme allowing for robust automatic personalization

2007 ◽  
Author(s):  
Sotirios Chatzis ◽  
Anastasios Doulamis ◽  
Theodora Varvarigou
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Retrieval Scheme

A FAST TWO-STAGE CONTENT-BASED IMAGE RETRIEVAL APPROACH IN THE DCT DOMAIN

2008 ◽  
Vol 22 (04) ◽  
pp. 765-781 ◽  
Author(s):  
TIENWEI TSAI ◽  
YO-PING HUANG ◽  
TE-WEI CHIANG
Keyword(s):  
Image Retrieval ◽  
High Efficiency ◽  
Feature Space ◽  
Content Based Image Retrieval ◽  
Two Stage ◽  
Feature Extraction Method ◽  
Domain Specific ◽  
Retrieval Scheme ◽  
Domain Specific Knowledge ◽  
Distinct Features

In this paper, a two-stage content-based image retrieval (CBIR) approach is proposed to improve the retrieval performance. To develop a general retrieval scheme which is less dependent on domain-specific knowledge, the discrete cosine transform (DCT) is employed as a feature extraction method. In establishing the database, the DC coefficients of Y, U and V components are quantized such that the feature space is partitioned into a finite number of grids, each of which is mapped to a grid code (GC). When querying an image, at coarse classification stage, the grid-based classification (GBC) and the distance threshold pruning (DTP) serve as a filter to remove those candidates with widely distinct features. At the fine classification stage, only the remaining candidates need to be computed for the detailed similarity comparison. The experimental results show that both high efficacy and high efficiency can be achieved simultaneously using the proposed two-stage approach.

A Privacy-Preserving and Copy-Deterrence Content-Based Image Retrieval Scheme in Cloud Computing

2016 ◽  
Vol 11 (11) ◽  
pp. 2594-2608 ◽  
Author(s):  
Zhihua Xia ◽  
Xinhui Wang ◽  
Liangao Zhang ◽  
Zhan Qin ◽  
Xingming Sun ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Image Retrieval ◽  
Privacy Preserving ◽  
Content Based Image Retrieval ◽  
Retrieval Scheme

Images Retrieval Based on Integrated Features

2014 ◽  
Vol 543-547 ◽  
pp. 2292-2295
Author(s):  
Ching Hung Su ◽  
Huang Sen Chiu ◽  
Mohd Helmy A. Wahab ◽  
Tsai Ming Hsiehb ◽  
You Chiuan Li ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Texture Feature ◽  
Content Based Image Retrieval ◽  
Code Comparison ◽  
Retrieval Scheme ◽  
Image Retrieval System ◽  
Retrieval Problem ◽  
Color Sequence ◽  
Edge Histogram

We propose a practical image retrieval scheme to retrieve images efficiently. The proposed scheme transfers each image to a color sequence using straightforward 8 rules. Subsequently, using the color sequences to compare the images, namely color sequences comparison. We succeed in transferring the image retrieval problem to sequences comparison and subsequently using the color sequences comparison along with the texture feature of Edge Histogram Descriptor to compare the images of database. We succeed in transferring the image retrieval problem to quantized code comparison. Thus the computational complexity is decreased obviously. Our results illustrate it has virtues both of the content based image retrieval system and a text based image retrieval system.

Personalized Content-Based Image Retrieval

2011 ◽  
pp. 194-219 ◽  
Author(s):  
Iker Gondra
Keyword(s):  
Image Retrieval ◽  
Similarity Measure ◽  
Semantic Interpretation ◽  
Semantic Gap ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Retrieval Strategy ◽  
Visual Content ◽  
Low Level ◽  
Retrieval Scheme

In content-based image retrieval (CBIR), a set of low-level features are extracted from an image to represent its visual content. Retrieval is performed by image example where a query image is given as input by the user and an appropriate similarity measure is used to find the best matches in the corresponding feature space. This approach suffers from the fact that there is a large discrepancy between the low-level visual features that one can extract from an image and the semantic interpretation of the image’s content that a particular user may have in a given situation. That is, users seek semantic similarity, but we can only provide similarity based on low-level visual features extracted from the raw pixel data, a situation known as the semantic gap. The selection of an appropriate similarity measure is thus an important problem. Since visual content can be represented by different attributes, the combination and importance of each set of features varies according to the user’s semantic intent. Thus, the retrieval strategy should be adaptive so that it can accommodate the preferences of different users. Relevance feedback (RF) learning has been proposed as a technique aimed at reducing the semantic gap. It works by gathering semantic information from user interaction. Based on the user’s feedback on the retrieval results, the retrieval scheme is adjusted. By providing an image similarity measure under human perception, RF learning can be seen as a form of supervised learning that finds relations between high-level semantic interpretations and low-level visual properties. That is, the feedback obtained within a single query session is used to personalize the retrieval strategy and thus enhance retrieval performance. In this chapter we present an overview of CBIR and related work on RF learning. We also present our own previous work on a RF learning-based probabilistic region relevance learning algorithm for automatically estimating the importance of each region in an image based on the user’s semantic intent.

An Efficient Content Based Image Retrieval Scheme

2013 ◽  
Vol 11 (11) ◽  
Author(s):  
Zukuan WEI ◽  
Hongyeon KIM ◽  
Youngkyun KIM ◽  
Jaehong KIM
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Retrieval Scheme
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