scholarly journals Image Retrieval Based on Fractal Dictionary Parameters

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Yuanyuan Sun ◽  
Rudan Xu ◽  
Lina Chen ◽  
Xiaopeng Hu
Keyword(s):  
Computer Vision ◽  
Image Compression ◽  
Image Retrieval ◽  
Experimental Results ◽  
Content Based Image Retrieval ◽  
Efficient Management ◽  
Retrieval Rate ◽  
Statistical Indices ◽  
Visual Database

Content-based image retrieval is a branch of computer vision. It is important for efficient management of a visual database. In most cases, image retrieval is based on image compression. In this paper, we use a fractal dictionary to encode images. Based on this technique, we propose a set of statistical indices for efficient image retrieval. Experimental results on a database of 416 texture images indicate that the proposed method provides a competitive retrieval rate, compared to the existing methods.

Comparison of Color, Texture and ICM Features in CBIR System

2011 ◽  
Vol 403-408 ◽  
pp. 13-19 ◽  
Author(s):  
Sonali Bhadoria ◽  
Meenakshi Madugunki ◽  
C.G. Dethe ◽  
Preeti Aggarwal
Keyword(s):  
Computer Vision ◽  
Image Retrieval ◽  
Data Base ◽  
Image Databases ◽  
Content Based Image Retrieval ◽  
Retrieval Process ◽  
Retrieval Performance ◽  
Research Areas ◽  
Matching Techniques ◽  
Index Search

Image retrieval has been one of the most interesting and vivid research areas in the field of computer vision over the last decades. Content-Based Image Retrieval (CBIR) systems are used in order to automatically index, search, retrieve, and browse image databases. There are various features which can be extracted from the image which gives different performance in retrieving the image.al systems. In this paper we have tried to compare the effect of using different features on the same data base to implement CBIR system. We have tried to analyse the retrieval performance for each feature. We have compared different features as well as the combinations of them to improve the performance. We have also compared the effect of different matching techniques on the retrieval process.

Image Retrieval Techniques Using Content-Based Local Binary Descriptors

2017 ◽  
pp. 173-195
Author(s):  
Rakesh Asery ◽  
Ramesh Kumar Sunkaria ◽  
Puneeta Marwaha ◽  
Lakhan Dev Sharma
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Gabor Transform ◽  
Comparative Experiment ◽  
Query Image ◽  
Binary Descriptors ◽  
Retrieval Rate ◽  
Retrieval Systems ◽  
Different Content

In this chapter authors introduces content-based image retrieval systems and compares them over a common database. For this, four different content-based local binary descriptors are described with and without Gabor transform in brief. Further Nth derivative descriptor is calculated using (N-1)th derivative, based on rotational and multiscale feature extraction. At last the distance based query image matching is used to find the similarity with database. The performance in terms of average precision, average retrieval rate, different orders of derivatives in the form of average retrieval rate, and length of feature vector v/s performance in terms of time have been calculated. For this work a comparative experiment has been conducted using the Ponce Group images on seven classes (each class have 100 images). In addition, the performance of the all descriptors have been analyzed by combining these with the Gabor transform.

scholarly journals Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Afshan Latif ◽  
Aqsa Rasheed ◽  
Umer Sajid ◽  
Jameel Ahmed ◽  
Nouman Ali ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Image Classification ◽  
Image Representation ◽  
Feature Representation ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Comprehensive Review ◽  
Visual Understanding

Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. In last few years, the complexity of multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram. To search for a relevant image from an archive is a challenging research problem for computer vision research community. Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata. In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis. In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values. The research shows that there is a significant gap between image feature representation and human visual understanding. Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding. In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation. We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.

A Retrieval Strategy for Texture Image

2014 ◽  
Vol 635-637 ◽  
pp. 1018-1025
Author(s):  
Chun Hua Qian ◽  
He Qun Qiang ◽  
Sheng Rong Gong
Keyword(s):  
Image Retrieval ◽  
Image Database ◽  
Image Space ◽  
Experimental Results ◽  
Content Based Image Retrieval ◽  
Texture Image ◽  
Retrieval Strategy ◽  
Wold Decomposition ◽  
Texture Information

Texture Information is widely used as one of the main low-layer features in the content-based image retrieval. In general, when the retrieval is carried out in texture image space, the same description method is adopted to regular and irregular texture images. As a large amount of regular and irregular texture images existed in the image database, it is very difficult to describe every texture with the same description method. In this paper, a retrieval strategy for texture image is proposed. The proposed strategy is divided into steps: First, classify texture images by Wold decomposition into regular and irregular texture images, then describe and retrieve them by regular and irregular texture description separately. Experimental results have showed that proposed strategy can improve classification and retrieval precision.

scholarly journals Integration of Statistical based Texture and Color Feature for Medical Image Retrieval

2019 ◽  
Vol 8 (3) ◽  
pp. 5584-5588 ◽  
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Image Retrieval ◽  
Medical Image ◽  
Spatial Information ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Retrieval Method ◽  
Medical Image Retrieval ◽  
The Common

Today, the common problem in the domain of computer vision and pattern recognition is content based image retrieval (CBIR). In this paper, a novel image retrieval method using the geometric details based on the correlation among edgels and correlation between pixels has been introduced. The autocorrelation based choridiogram descriptor has been extracted from the image to obtain geometric, texture and spatial information. Color autocorrelogram has been computed to obtain color, texture and spatial information. The proposed method is tested on benchmark heterogeneous medical image database and LIDC-IDRI-CT and VIA/I-ELCAP-CT databases and results are compared with typical CBIR system for medical image retrieval

scholarly journals A Method for Recognizing and Separating Trademark Image Outer Frames

2002 ◽  
Vol 6 (3) ◽  
pp. 116-125
Author(s):  
Koji Abe ◽  
◽  
Haruhiko Kimura ◽  
Hideo Nagashima ◽  
Taki Kanda ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Experimental Evaluation ◽  
Human Perception ◽  
Experimental Results ◽  
Content Based Image Retrieval ◽  
Registered Trademark ◽  
Outer Frame ◽  
Trademark Registration ◽  
Trademark Images

We present a method for recognizing the existence of outer frames in binary trademark images and segmenting a trademark that contains an outer frame into the frame and its inner figure, even if both touch. This focuses on the development of content-based image retrieval (CBIR) for trademark registration. Using our proposed method, CBIR systems examine the similarity between images using only main image components. This includes a study for describing image components. We detail criteria of trademark image outer frames and propose an algorithm for recognizing and segmenting outer frames based on the criteria. Experimental results using 1843 registered trademark images and experimental evaluation by 13 participants showed that 98.4% of recognitions agreed with human perception.

scholarly journals Feature Fusion for Image Retrieval using Image Processing

2019 ◽  
Vol 8 (12) ◽  
pp. 3157-3162
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Retrieval ◽  
Principle Component Analysis ◽  
Clustering Algorithm ◽  
Feature Fusion ◽  
The Other ◽  
Content Based Image Retrieval ◽  
Huge Amount ◽  
Attribute Data

Image processing and computer vision uses Content-based image retrieval (CBIR) function to solve the issue of image retrieval, which means, solving the issue of image searching in expansive databases. The actual data of the image will be evaluated when a search is performed that refers to content-based. The term content can be any attribute of an image like colour-shade, various symbols or shapes, sizes, or any other data. There are various approaches for image retrieval but the most prominent are by comparing the main image with the subsets of the relatable images whether it matches or not and the other one is by using a matching descriptor for the image. One of the main trouble for huge amount of CBIR is the representation of an image. When a given image is worked upon it is divided into number of attributes in which some are the primary ones and others are the secondary ones. These attributes are checked with the local and MPEG-7 descriptors. All this is then mapped in a single vector which is the same images but in compact form to save the space. Principle Component Analysis (PCA) is used lessen the attribute size. To store the attribute data in similar clusters and to train them to give the correct output the study also uses k-means clustering algorithm. Hence, the proposed system deals with the image retrieval using various algorithms and methods.

CBIR-CNN: Content Based Image Retrieval on celebrity data using Deep Convolution Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Pushpendra Singh ◽  
P.N. Hrisheekesha ◽  
Vinai Kumar Singh
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Image Retrieval ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Facial Image ◽  
Huge Amount ◽  
Proposed Model ◽  
Deep Convolution Neural Network

Background: Finding region of interest in an image and content-based image analysis has been a challenging task for last two decades. With the advancement in image processing, computer vision field and huge amount of image data generation, to manage this huge amount of data Content-Based Image Retrieval System (CBIR) has attracted several researchers as a common technique to manage this huge amount of data. It is an approach of searching user interest, based on visual information present in an image. The requirement of high computation power and huge memory limits deployment of CBIR technique in real-time scenarios. Objective: In this paper an advanced deep learning model is applied for CBIR on facial image data. We design a deep convolution neural network architecture where activation of convolution layer is used for feature representation and include max pooling as feature reduction technique. Furthermore, our model uses partial feature mapping as image descriptor to incorporate the property that facial image contains repeated information. Method: Existing CBIR approaches primarily consider colour, texture and low-level features for mapping and localizing image segments. While deep learning has shown high performance in numerous fields of research, its application in CBIR is still very limited. Human face contains significant information to be used in a content driven task and applicable to various applications of computer vision and multimedia systems. In this research work, a deep learning-based model has been discussed for content-based image retrieval (CBIR). In CBIR, there are two important things 1) classification and 2) retrieval of image based on similarity. For the classification purpose a four-convolution layer model has been proposed. For the calculation of the similarity Euclidian distance measure has been used between the images. Results: Proposed model is completely unsupervised, and it is fast and accurate in comparison to other deep learning models applied for CBIR over facial dataset. The proposed method provided satisfactory results from the experiment. It outperforms other CNN-based models and other unsupervised techniques used for CBIR. The proposed method provided satisfactory results from the experiment and it outperforms other CNN-based models such as VGG16, Inception V3, ResNet50 and MobileNet. Moreover, the performance of proposed model has been compared with pre-trained models in terms of accuracy, storage space and inference time.

AUTOMATIC CLASSIFICATION OF DIGITAL PHOTOGRAPHS BASED ON DECISION FORESTS

2004 ◽  
Vol 18 (05) ◽  
pp. 819-845 ◽  
Author(s):  
RAIMONDO SCHETTINI ◽  
CARLA BRAMBILLA ◽  
CLAUDIO CUSANO ◽  
GIANLUIGI CIOCCA
Keyword(s):  
Image Processing ◽  
Image Retrieval ◽  
Automatic Classification ◽  
Experimental Results ◽  
Content Based Image Retrieval ◽  
Test Set ◽  
Digital Photographs ◽  
Low Level

Annotating photographs with broad semantic labels can be useful in both image processing and content-based image retrieval. We show here how low-level features can be related to semantic photo categories, such as indoor, outdoor and close-up, using decision forests consisting of trees constructed according to CART methodology. We also show how the results can be improved by introducing a rejection option in the classification process. Experimental results on a test set of 4,500 photographs are reported and discussed.

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