A multi-sample, multi-tree approach to bag-of-words image representation for image retrieval

The advancements in the multimedia technologies result in the growth of the image databases. To retrieve images from such image databases using visual attributes of the images is a challenging task due to the close visual appearance among the visual attributes of these images, which also introduces the issue of the semantic gap. In this article, we recommend a novel method established on the bag-of-words (BoW) model, which perform visual words integration of the local intensity order pattern (LIOP) feature and local binary pattern variance (LBPV) feature to reduce the issue of the semantic gap and enhance the performance of the content-based image retrieval (CBIR). The recommended method uses LIOP and LBPV features to build two smaller size visual vocabularies (one from each feature), which are integrated together to build a larger size of the visual vocabulary, which also contains complementary features of both descriptors. Because for efficient CBIR, the smaller size of the visual vocabulary improves the recall, while the bigger size of the visual vocabulary improves the precision or accuracy of the CBIR. The comparative analysis of the recommended method is performed on three image databases, namely, WANG-1K, WANG-1.5K and Holidays. The experimental analysis of the recommended method on these image databases proves its robust performance as compared with the recent CBIR methods.

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A Combination of Spatial Pyramid and Inverted Index for Large-Scale Image Retrieval

Computer Vision ◽

10.4018/978-1-5225-5204-8.ch054 ◽

2018 ◽

pp. 1307-1321

Author(s):

Vinh-Tiep Nguyen ◽

Thanh Duc Ngo ◽

Minh-Triet Tran ◽

Duy-Dinh Le ◽

Duc Anh Duong

Keyword(s):

Image Retrieval ◽

Large Scale ◽

Spatial Information ◽

Real Life ◽

Inverted Index ◽

Bag Of Words ◽

Visual Words ◽

Benchmark Datasets ◽

Large Scale Image Retrieval ◽

Inverted Indexing

Large-scale image retrieval has been shown remarkable potential in real-life applications. The standard approach is based on Inverted Indexing, given images are represented using Bag-of-Words model. However, one major limitation of both Inverted Index and Bag-of-Words presentation is that they ignore spatial information of visual words in image presentation and comparison. As a result, retrieval accuracy is decreased. In this paper, the authors investigate an approach to integrate spatial information into Inverted Index to improve accuracy while maintaining short retrieval time. Experiments conducted on several benchmark datasets (Oxford Building 5K, Oxford Building 5K+100K and Paris 6K) demonstrate the effectiveness of our proposed approach.

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A Perceptual Approach for Image Representation and Retrieval

Computer Vision for Multimedia Applications ◽

10.4018/978-1-60960-024-2.ch008 ◽

2011 ◽

pp. 121-141

Author(s):

Noureddine Abbadeni

Keyword(s):

Image Retrieval ◽

Image Representation ◽

Rank Correlation ◽

Human Perception ◽

Image Data ◽

Content Based Image Retrieval ◽

Data Set ◽

Psychometric Method ◽

Results Merging ◽

Spearman Rank Correlation Coefficient

This chapter describes an approach based on human perception to content-based image representation and retrieval. We consider textured images and propose to model the textural content of images by a set of features having a perceptual meaning and their application to content-based image retrieval. We present a new method to estimate a set of perceptual textural features, namely coarseness, directionality, contrast and busyness. The proposed computational measures are based on two representations: the original images representation and the autocovariance function (associated with images) representation. The correspondence of the proposed computational measures to human judgments is shown using a psychometric method based on the Spearman rank-correlation coefficient. The set of computational measures is applied to content-based image retrieval on a large image data set, the well-known Brodatz database. Experimental results show a strong correlation between the proposed computational textural measures and human perceptual judgments. The benchmarking of retrieval performance, done using the recall measure, shows interesting results. Furthermore, results merging/fusion returned by each of the two representations is shown to allow significant improvement in retrieval effectiveness.

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A Combination of Spatial Pyramid and Inverted Index for Large-Scale Image Retrieval

International Journal of Multimedia Data Engineering and Management ◽

10.4018/ijmdem.2015040103 ◽

2015 ◽

Vol 6 (2) ◽

pp. 37-51 ◽

Cited By ~ 2

Author(s):

Vinh-Tiep Nguyen ◽

Thanh Duc Ngo ◽

Minh-Triet Tran ◽

Duy-Dinh Le ◽

Duc Anh Duong

Keyword(s):

Image Retrieval ◽

Large Scale ◽

Spatial Information ◽

Real Life ◽

Inverted Index ◽

Bag Of Words ◽

Visual Words ◽

Benchmark Datasets ◽

Large Scale Image Retrieval ◽

Inverted Indexing

Large-scale image retrieval has been shown remarkable potential in real-life applications. The standard approach is based on Inverted Indexing, given images are represented using Bag-of-Words model. However, one major limitation of both Inverted Index and Bag-of-Words presentation is that they ignore spatial information of visual words in image presentation and comparison. As a result, retrieval accuracy is decreased. In this paper, the authors investigate an approach to integrate spatial information into Inverted Index to improve accuracy while maintaining short retrieval time. Experiments conducted on several benchmark datasets (Oxford Building 5K, Oxford Building 5K+100K and Paris 6K) demonstrate the effectiveness of our proposed approach.

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A document expansion framework for tag-based image retrieval

Aslib Journal of Information Management ◽

10.1108/ajim-05-2017-0133 ◽

2018 ◽

Vol 70 (1) ◽

pp. 47-65 ◽

Cited By ~ 1

Author(s):

Wei Lu ◽

Heng Ding ◽

Jiepu Jiang

Keyword(s):

Image Retrieval ◽

Nearest Neighbor ◽

Image Representation ◽

Image Features ◽

Mobile Platforms ◽

Content Type ◽

Expansion Strategy ◽

Rank List ◽

Core Components ◽

Similar Images

Purpose The purpose of this paper is to utilize document expansion techniques for improving image representation and retrieval. This paper proposes a concise framework for tag-based image retrieval (TBIR). Design/methodology/approach The proposed approach includes three core components: a strategy of selecting expansion (similar) images from the whole corpus (e.g. cluster-based or nearest neighbor-based); a technique for assessing image similarity, which is adopted for selecting expansion images (text, image, or mixed); and a model for matching the expanded image representation with the search query (merging or separate). Findings The results show that applying the proposed method yields significant improvements in effectiveness, and the method obtains better performance on the top of the rank and makes a great improvement on some topics with zero score in baseline. Moreover, nearest neighbor-based expansion strategy outperforms the cluster-based expansion strategy, and using image features for selecting expansion images is better than using text features in most cases, and the separate method for calculating the augmented probability P(q|RD) is able to erase the negative influences of error images in RD. Research limitations/implications Despite these methods only outperform on the top of the rank instead of the entire rank list, TBIR on mobile platforms still can benefit from this approach. Originality/value Unlike former studies addressing the sparsity, vocabulary mismatch, and tag relatedness in TBIR individually, the approach proposed by this paper addresses all these issues with a single document expansion framework. It is a comprehensive investigation of document expansion techniques in TBIR.

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Learning image representation from image reconstruction for a content-based medical image retrieval

Signal Image and Video Processing ◽

10.1007/s11760-020-01670-y ◽

2020 ◽

Vol 14 (7) ◽

pp. 1319-1326

Author(s):

Rohini Pinapatruni ◽

C. Shoba Bindu

Keyword(s):

Image Reconstruction ◽

Image Retrieval ◽

Medical Image ◽

Image Representation ◽

Medical Image Retrieval

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CONTENT-BASED IMAGE RETRIEVAL WITH RELEVANCE FEEDBACK USING ADAPTIVE PROCESSING OF TREE-STRUCTURE IMAGE REPRESENTATION

International Journal of Image and Graphics ◽

10.1142/s0219467803000944 ◽

2003 ◽

Vol 03 (01) ◽

pp. 119-143 ◽

Cited By ~ 6

Author(s):

ZHIYONG WANG ◽

ZHERU CHI ◽

DAGAN FENG ◽

AH CHUNG TSOI

Keyword(s):

Neural Network ◽

Image Retrieval ◽

Network Model ◽

Neural Network Model ◽

Relevance Feedback ◽

Image Representation ◽

Tree Structure ◽

Content Based Image Retrieval ◽

Adaptive Processing ◽

Image Content

Content-based image retrieval has become an essential technique in multimedia data management. However, due to the difficulties and complications involved in the various image processing tasks, a robust semantic representation of image content is still very difficult (if not impossible) to achieve. In this paper, we propose a novel content-based image retrieval approach with relevance feedback using adaptive processing of tree-structure image representation. In our approach, each image is first represented with a quad-tree, which is segmentation free. Then a neural network model with the Back-Propagation Through Structure (BPTS) learning algorithm is employed to learn the tree-structure representation of the image content. This approach that integrates image representation and similarity measure in a single framework is applied to the relevance feedback of the content-based image retrieval. In our approach, an initial ranking of the database images is first carried out based on the similarity between the query image and each of the database images according to global features. The user is then asked to categorize the top retrieved images into similar and dissimilar groups. Finally, the BPTS neural network model is used to learn the user's intention for a better retrieval result. This process continues until satisfactory retrieval results are achieved. In the refining process, a fine similarity grading scheme can also be adopted to improve the retrieval performance. Simulations on texture images and scenery pictures have demonstrated promising results which compare favorably with the other relevance feedback methods tested.

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Bag-of-Words Representation in Image Annotation: A Review

ISRN Artificial Intelligence ◽

10.5402/2012/376804 ◽

2012 ◽

Vol 2012 ◽

pp. 1-19 ◽

Cited By ~ 75

Author(s):

Chih-Fong Tsai

Keyword(s):

Image Retrieval ◽

Image Annotation ◽

Classification Problem ◽

Feature Representation ◽

Future Research ◽

Bag Of Words ◽

Low Level ◽

Learning Techniques ◽

Class Labels ◽

High Level

Content-based image retrieval (CBIR) systems require users to query images by their low-level visual content; this not only makes it hard for users to formulate queries, but also can lead to unsatisfied retrieval results. To this end, image annotation was proposed. The aim of image annotation is to automatically assign keywords to images, so image retrieval users are able to query images by keywords. Image annotation can be regarded as the image classification problem: that images are represented by some low-level features and some supervised learning techniques are used to learn the mapping between low-level features and high-level concepts (i.e., class labels). One of the most widely used feature representation methods is bag-of-words (BoW). This paper reviews related works based on the issues of improving and/or applying BoW for image annotation. Moreover, many recent works (from 2006 to 2012) are compared in terms of the methodology of BoW feature generation and experimental design. In addition, several different issues in using BoW are discussed, and some important issues for future research are discussed.

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