A New Content-Based Image Retrieval System Using Deep Visual Features

2021 ◽  
pp. 2150055
Author(s):  
Mohamed Hamroun ◽  
Karim Tamine ◽  
Frederic Claux ◽  
Mourad Zribi
Keyword(s):  
Image Retrieval ◽  
Feature Vector ◽  
Texture Features ◽  
Pso Algorithm ◽  
Image Feature ◽  
Combination Method ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Average Precision ◽  
Food Category

Content-based image retrieval (CBIR) is a technique for images retrieval based on their visual features, i.e. induced by their pixels. The images are, classically, described by the image feature vectors. Those vectors reflect the texture, color or a combination of them. The accuracy of the CBIR system is highly influenced by the (i) definition of the image feature vector describing the image, (ii) indexing and (iii) retrieval process. In this paper, we propose a new CBIR system entitled ISE (Image Search Engine). Our ISE system defines the optimum combination of color and texture features as an image feature vector, including the Particle Swarm Optimization (PSO) algorithm and employing an Interactive Genetic Approach (GA) for the indexing process. The performance analysis shows that our suggested PCM (Proposed Combination Method) upgrades the average precision metric from 66.6% to 89.30% for the “Food” category color histogram, from 77.7% to 100% concerning CCVs (Color Coherence Vectors) for the “Flower” category and from 58% to 87.65% regarding the DCD (Dominant Color Descriptor) for the “Building” category using the Corel dataset. Besides, our ISE system showcases an average precision of 98.23%, which is significantly higher than other CBIR systems presented in related works.

Content-based image retrieval using local texture features in distributed environment

2019 ◽  
Vol 18 (01) ◽  
pp. 1941001
Author(s):  
U. S. N. Raju ◽  
K. Suresh Kumar ◽  
Pulkesh Haran ◽  
Ramya Sree Boppana ◽  
Niraj Kumar
Keyword(s):  
Image Retrieval ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Texture Features ◽  
Content Based Image Retrieval ◽  
Distributed Environment ◽  
Average Precision ◽  
Neighboring Pixel ◽  
Hadoop Cluster ◽  
The Relationship

In this paper, we propose novel content-based image retrieval (CBIR) algorithms using Local Octa Patterns (LOtP), Local Hexadeca Patterns (LHdP) and Direction Encoded Local Binary Pattern (DELBP). LOtP and LHdP encode the relationship between center pixel and its neighbors based on the pixels’ direction obtained by considering the horizontal, vertical and diagonal pixels for derivative calculations. In DELBP, direction of a referenced pixel is determined by considering every neighboring pixel for derivative calculations which results in 256 directions. For this resultant direction encoded image, we have obtained LBP which is considered as feature vector. The proposed method’s performance is compared to that of Local Tetra Patterns (LTrP) using benchmark image databases viz., Corel 1000 (DB1) and Brodatz textures (DB2). Performance analysis shows that LOtP improves the average precision from 59.31% to 64.36% on DB1, and from 83.24% to 85.95% on DB2, LHdP improves it to 65.82% on DB1 and to 87.49% on DB2 and DELBP improves it to 60.35% on DB1 and to 86.12% on DB2 as compared to that of LTrP. Also, DELBP reduces the feature vector length by 66.62% as compared to that of LTrP. To reduce the retrieval time, the proposed algorithms are implemented on a Hadoop cluster consisting of 116 nodes and tested using Corel 10K (DB3), Mirflickr 100,000 (DB4) and ImageNet 511,380 (DB5) databases.

Content-based Image Retrieval by Information Theoretic Measure

2011 ◽  
Vol 61 (5) ◽  
pp. 415 ◽  
Author(s):  
Madasu Hanmandlu ◽  
Anirban Das
Keyword(s):  
Image Retrieval ◽  
Feature Vector ◽  
Texture Features ◽  
Entropy Function ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Information Theoretic ◽  
Dominant Colour ◽  
Image Retrieval System ◽  
Information Theoretic Measures

<p>Content-based image retrieval focuses on intuitive and efficient methods for retrieving images from databases based on the content of the images. A new entropy function that serves as a measure of information content in an image termed as 'an information theoretic measure' is devised in this paper. Among the various query paradigms, 'query by example' (QBE) is adopted to set a query image for retrieval from a large image database. In this paper, colour and texture features are extracted using the new entropy function and the dominant colour is considered as a visual feature for a particular set of images. Thus colour and texture features constitute the two-dimensional feature vector for indexing the images. The low dimensionality of the feature vector speeds up the atomic query. Indices in a large database system help retrieve the images relevant to the query image without looking at every image in the database. The entropy values of colour and texture and the dominant colour are considered for measuring the similarity. The utility of the proposed image retrieval system based on the information theoretic measures is demonstrated on a benchmark dataset.</p><p><strong>Defence Science Journal, 2011, 61(5), pp.415-430</strong><strong><strong>, DOI:http://dx.doi.org/10.14429/dsj.61.1177</strong></strong></p>

scholarly journals Integration of Discrete Wavelet Transform, DBSCAN, and Classifiers for Efficient Content Based Image Retrieval

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1886
Author(s):  
Muhammad Junaid Khalid ◽  
Muhammad Irfan ◽  
Tariq Ali ◽  
Muqaddas Gull ◽  
Umar Draz ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Discrete Wavelet Transform ◽  
Feature Vector ◽  
Similarity Index ◽  
Machine Learning Algorithms ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Discrete Wavelet

In the domain of computer vision, the efficient representation of an image feature vector for the retrieval of images remains a significant problem. Extensive research has been undertaken on Content-Based Image Retrieval (CBIR) using various descriptors, and machine learning algorithms with certain descriptors have significantly improved the performance of these systems. In this proposed research, a new scheme for CBIR was implemented to address the semantic gap issue and to form an efficient feature vector. This technique was based on the histogram formation of query and dataset images. The auto-correlogram of the images was computed w.r.t RGB format, followed by a moment’s extraction. To form efficient feature vectors, Discrete Wavelet Transform (DWT) in a multi-resolution framework was applied. A codebook was formed using a density-based clustering approach known as Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The similarity index was computed using the Euclidean distance between the feature vector of the query image and the dataset images. Different classifiers, like Support Vector (SVM), K-Nearest Neighbor (KNN), and Decision Tree, were used for the classification of images. The set experiment was performed on three publicly available datasets, and the performance of the proposed framework was compared with another state of the proposed frameworks which have had a positive performance in terms of accuracy.

Enhanced Content-Based Image Retrieval Using Information Oriented Angle-Based Local Tri-Directional Weber Patterns

2021 ◽  
pp. 2150046
Author(s):  
Gangavarapu Venkata Satya Kumar ◽  
Pillutla Gopala Krishna Mohan
Keyword(s):  
Image Retrieval ◽  
Computer Applications ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
The Novel ◽  
Image Content ◽  
Query Image ◽  
Proposed Model ◽  
Maximum Mutual Information ◽  
Basic Features

In diverse computer applications, the analysis of image content plays a key role. This image content might be either textual (like text appearing in the images) or visual (like shape, color, texture). These two image contents consist of image’s basic features and therefore turn out to be as the major advantage for any of the implementation. Many of the art models are based on the visual search or annotated text for Content-Based Image Retrieval (CBIR) models. There is more demand toward multitasking, a new method needs to be introduced with the combination of both textual and visual features. This paper plans to develop the intelligent CBIR system for the collection of different benchmark texture datasets. Here, a new descriptor named Information Oriented Angle-based Local Tri-directional Weber Patterns (IOA-LTriWPs) is adopted. The pattern is operated not only based on tri-direction and eight neighborhood pixels but also based on four angles [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]. Once the patterns concerning tri-direction, eight neighborhood pixels, and four angles are taken, the best patterns are selected based on maximum mutual information. Moreover, the histogram computation of the patterns provides the final feature vector, from which the new weighted feature extraction is performed. As a new contribution, the novel weight function is optimized by the Improved MVO on random basis (IMVO-RB), in such a way that the precision and recall of the retrieved image is high. Further, the proposed model has used the logarithmic similarity called Mean Square Logarithmic Error (MSLE) between the features of the query image and trained images for retrieving the concerned images. The analyses on diverse texture image datasets have validated the accuracy and efficiency of the developed pattern over existing.

scholarly journals Implementation of early and late fusion methods for content-based image retrieval

2021 ◽  
Vol 8 (7) ◽  
pp. 97-105
Author(s):  
Ali Ahmed ◽  
◽  
Sara Mohamed ◽  
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Image Features ◽  
Distance Measures ◽  
Content Based Image Retrieval ◽  
Late Fusion ◽  
Query Image ◽  
Fusion Methods ◽  
And Performance ◽  
Image Repository

Content-Based Image Retrieval (CBIR) systems retrieve images from the image repository or database in which they are visually similar to the query image. CBIR plays an important role in various fields such as medical diagnosis, crime prevention, web-based searching, and architecture. CBIR consists mainly of two stages: The first is the extraction of features and the second is the matching of similarities. There are several ways to improve the efficiency and performance of CBIR, such as segmentation, relevance feedback, expansion of queries, and fusion-based methods. The literature has suggested several methods for combining and fusing various image descriptors. In general, fusion strategies are typically divided into two groups, namely early and late fusion strategies. Early fusion is the combination of image features from more than one descriptor into a single vector before the similarity computation, while late fusion refers either to the combination of outputs produced by various retrieval systems or to the combination of different rankings of similarity. In this study, a group of color and texture features is proposed to be used for both methods of fusion strategies. Firstly, an early combination of eighteen color features and twelve texture features are combined into a single vector representation and secondly, the late fusion of three of the most common distance measures are used in the late fusion stage. Our experimental results on two common image datasets show that our proposed method has good performance retrieval results compared to the traditional way of using single features descriptor and also has an acceptable retrieval performance compared to some of the state-of-the-art methods. The overall accuracy of our proposed method is 60.6% and 39.07% for Corel-1K and GHIM-10K ‎datasets, respectively.

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