Features In Content-based Image Retrieval Research Articles

Automatic Weight of Color, Texture, and Shape Features in Content-Based Image Retrieval Using Artificial Neural Network

Image retrieval is the process of finding images in the database that are similar to the query image by measuring how close the feature values of the query image are to other images. Image retrieval is currently dominated by approaches that combine several different representations or features. The optimal weight of each feature is needed in combining the image features such as color features, texture features, and shape features. In this study, we use a multi-layer perceptron artificial neural network (MLP) method to obtain feature weights automatically and simultaneously look for optimal weights. The color moment is used to find nine color features, Gray Level Co-occurrence Matrix (GLCM) to find four texture features, and Hu Moment to find seven shape features totaling 20 neurons and all of these features become the input layer in our MLP network. Three neurons in output layers become the automatic weight of each feature. These weights are used to combine each feature's role in obtaining the relevant image. Euclidean Distance is used to measure similarity. The average precision values obtained using automatic feature weights are 93.94% for the synthetic dataset, 91.19% for the Coil-100 dataset, and 54.31% for the Wang dataset. These results have an average difference of 5.06% with the target so automatic feature weighting works well. This value is obtained at a hidden layer size of 11 and a learning rate of 0.1. In addition, the use of automatic feature weighting gives more accurate results compared to manual feature weighting.

Innovative local texture descriptor in joint of human-based color features for content-based image retrieval

Innovative local texture descriptor in joint of human-based color features for content-based image retrieval

An Effective Hybrid Framework Based on Combination of Color and Texture Features for Content-Based Image Retrieval

An Effective Hybrid Framework Based on Combination of Color and Texture Features for Content-Based Image Retrieval

Evaluating the use of Instagram images color histograms and hashtags sets for automatic image annotation.

Color similarity has been a key feature for content-based image retrieval by contemporary search engines, such as Google. In this study, we compare the visual content information of images, obtained through color histograms, with their corresponding hashtag sets in the case of Instagram posts. In previous studies, we had concluded that less than 25% of Instagram hashtags are related to the actual visual content of the image they accompany. Thus, the use of Instagram images' corresponding hashtags for automatic image annotation is questionable. In this study, we are answering this question through the computational comparison of images' low-level characteristics with the semantic and syntactic information of their corresponding hashtags. The main conclusion of our study on 26 different subjects (concepts) is that color histograms and filtered hashtag sets, although related, should be better seen as a complementary source for image retrieval and automatic image annotation.

Effective features in content-based image retrieval from a combination of low-level features and deep Boltzmann machine

Effective features in content-based image retrieval from a combination of low-level features and deep Boltzmann machine

Dynamic Mode Decomposition based salient edge/region features for content based image retrieval

Considering the gap between low-level image features and high-level retrieval concept, this paper investigates the effect of incorporating visual saliency based features for content-based image retrieval(CBIR).Visual saliency plays an important role in human perception due to its capability to focus the attention on the point of interest, i.e. an intended target. This selection based processing can be well explored in localized CBIR systems, since in context of CBIR the users will be interested only in certain parts of the image. The proposed methodology uses Dynamic Mode Decomposition framework to extract the saliency map which highlights the part of the image that grabs human attention. Then, based on the saliency map, an efficient salient edge detection model is introduced. Visual saliency based features (salient region, edge) are then combined with texture and color features to formulate the high dimensional feature vector for image retrieval. State-of-the-art learning based CBIR models demands for user feed back to model the retrieval concept. In contrast with these models, proposed CBIR system does not require any user interaction, since it uses perceptual level features for the retrieval task. Performance of the proposed CBIR system is evaluated and confirmed on images from Wang’s dataset using benchmark evaluation metrics like precision and recall. Experimental results reveals that incorporation of saliency features can represent human perception well and produces good retrieval performance.

Rethinking the Rotation Invariance of Local Convolutional Features for Content-Based Image Retrieval

Rethinking the Rotation Invariance of Local Convolutional Features for Content-Based Image Retrieval

Integration Colour and Texture Features for Content-based Image Retrieval

Integration Colour and Texture Features for Content-based Image Retrieval

An Appraisal of Content-Based Image Retrieval (CBIR) Methods

Background: Content Based Image Retrieval (CBIR) is an aspect of computer vision and image processing that finds images that are similar to a given query image in a large scale database using the visual contents of images such as colour, texture, shape, and spatial arrangement of regions of interest (ROIs) rather than manually annotated textual keywords. A CBIR system represents an image as a feature vector and measures the similarity between the image and other images in the database for the purpose of retrieving similar images with minimal human intervention. The CBIR system has been deployed in several fields such as fingerprint identification, biodiversity information systems, digital libraries, Architectural and Engineering design, crime prevention, historical research and medicine. There are several steps involved in the development of CBIR systems. Typical examples of these steps include feature extraction and selection, indexing and similarity measurement.
 Problem: However, each of these steps has its own method. Nevertheless, there is no universally acceptable method for retrieving similar images in CBIR.
 Aim: Hence, this study examines the diverse methods used in CBIR systems. This is with the aim of revealing the strengths and weakness of each of these methods.
 Methodology: Literatures that are related to the subject matter were sought in three scientific electronic databases namely CiteseerX, Science Direct and Google scholar. The Google search engine was used to search for documents and WebPages that are appropriate to the study.
 Results: The result of the study revealed that three main features are usually extracted during CBIR. These features include colour, shape and text. The study also revealed that diverse methods that can be used for extracting each of the features in CBIR. For instance, colour space, colour histogram, colour moments, geometric moment as well as colour correlogram can be used for extracting colour features. The commonly used methods for texture feature extraction include statistical, model-based, and transform-based methods while the edge method, Fourier transform and Zernike methods can be used for extracting shape features.
 Contributions: The paper highlights the benefits and challenges of diverse methods used in CBIR. This is with the aim of revealing the methods that are more efficient for CBIR.
 Conclusion: Each of the CBIR methods has their own advantages and disadvantages. However, there is a need for a further work that will validate the reliability and efficiency of each of the method.

Content-based image retrieval: a deep look at features prospectus

Currently, rapid growth of digital images on the internet is observed, accordingly, the need for content-based image retrieval systems are in high demand. Content-based image retrieval (CBIR) is an image search technique that does not depend on manually assigned annotations; rather, CBIR uses discriminative features to search an image. By refining features, an efficient retrieval mechanism could be achieved. The aim of this research is to review features extraction and selection that have an impact on content-based image retrieval (CBIR) and information extraction from images using global and local features such as shape, texture and colour. In order to extract most appropriate features for content-based image retrieval (CBIR), several feature extraction and selection techniques are analysed and their efficiency is compared. Additionally, shortcomings of current content-based image retrieval techniques are addressed and possible solutions are suggested to enhance accuracy.

Content-based image retrieval: a deep look at features prospectus

Currently, rapid growth of digital images on the internet is observed, accordingly, the need for content-based image retrieval systems are in high demand. Content-based image retrieval (CBIR) is an image search technique that does not depend on manually assigned annotations; rather, CBIR uses discriminative features to search an image. By refining features, an efficient retrieval mechanism could be achieved. The aim of this research is to review features extraction and selection that have an impact on content-based image retrieval (CBIR) and information extraction from images using global and local features such as shape, texture and colour. In order to extract most appropriate features for content-based image retrieval (CBIR), several feature extraction and selection techniques are analysed and their efficiency is compared. Additionally, shortcomings of current content-based image retrieval techniques are addressed and possible solutions are suggested to enhance accuracy.

Content-Based Image Retrieval Features: A Survey

Content-Based Image Retrieval (CBIR) systems have been used for the searching of relevant images in various research areas. In CBIR systems features such as shape, texture and color are used. The extraction of features is the main step on which the retrieval results depend. Color features in CBIR are used as in the color histogram, color moments, conventional color correlogram and color histogram. Color space selection is used to represent the information of color of the pixels of the query image. The shape is the basic characteristic of segmented regions of an image. Different methods are introduced for better retrieval using different shape representation techniques; earlier the global shape representations were used but with time moved towards local shape representations. The local shape is more related to the expressing of result instead of the method. Local shape features may be derived from the texture properties and the color derivatives. Texture features have been used for images of documents, segmentation-based recognition,and satellite images. Texture features are used in different CBIR systems along with color, shape, geometrical structure and sift features.

A Novel Method For CBIR Using ACO-SVM With DTCWT And Color Features

Content Based Image Retrieval (CBIR) has been the major thing for looking associated content material on the net in the form of pictures, knowledge and so on. A retrieval approach which organization color and texture feature is future in this paper. In this research, implement a new technique for CBIR by ant colony optimization (ACO) and Support vector machine (SVM) with dual tree complex wavelet transform (DTCWT) and color features.In this work, has been used color or texture features for CBIR. In the increase of feature extraction, firstly extract texture feature using DTCWT for resolving the problematic of redundant CWT. After that, extract color feature using dominant color descriptor (DCD) on RGB and HSV color space for improving computation and efficiency. The experimental dataset contain 1000 images including horses, elephants, food, African people, etc. The equal dimension is calculated using weighted Euclidean distance (WED). For improving effectiveness of the system, used ACO for the greatest result and classify data using SVM. The performance analysis is based on precision and F-measure.

Fusion of Deep Learning and Compressed Domain Features for Content-Based Image Retrieval.

This paper presents an effective image retrieval method by combining high-level features from convolutional neural network (CNN) model and low-level features from dot-diffused block truncation coding (DDBTC). The low-level features, e.g., texture and color, are constructed by vector quantization -indexed histogram from DDBTC bitmap, maximum, and minimum quantizers. Conversely, high-level features from CNN can effectively capture human perception. With the fusion of the DDBTC and CNN features, the extended deep learning two-layer codebook features is generated using the proposed two-layer codebook, dimension reduction, and similarity reweighting to improve the overall retrieval rate. Two metrics, average precision rate and average recall rate (ARR), are employed to examine various data sets. As documented in the experimental results, the proposed schemes can achieve superior performance compared with the state-of-the-art methods with either low-or high-level features in terms of the retrieval rate. Thus, it can be a strong candidate for various image retrieval related applications.

Content-Based Image Retrieval Using Iterative Search

The aim of Content-based Image Retrieval (CBIR) is to find a set of images that best match the query based on visual features. Most existing CBIR systems find similar images in low level features, while Text-based Image Retrieval (TBIR) systems find images with relevant tags regardless of contents in the images. Generally, people are more interested in images with similarity both in contours and high-level concepts. Therefore, we propose a new strategy called Iterative Search to meet this requirement. It mines knowledge from the similar images of original queries, in order to compensate for the missing information in feature extraction process. To evaluate the performance of Iterative Search approach, we apply this method to four different CBIR systems (HOF Zhou et al. in ACM international conference on multimedia, 2012; Zhou and Zhang in Neural information processing—international conference, ICONIP 2011, Shanghai, 2011, HOG Dalal and Triggs in IEEE computer society conference on computer vision pattern recognition, 2005, GIST Oliva and Torralba in Int J Comput Vision 42:145–175, 2001 and CNN Krizhevsky et al. in Adv Neural Inf Process Syst 25:2012, 2012) in our experiments. The results show that Iterative Search improves the performance of original CBIR features by about $$20\%$$ on both the Oxford Buildings dataset and the Object Sketches dataset. Meanwhile, it is not restricted to any particular visual features.

Content Based Image Retrieval using Feature Extraction in JPEG Domain and Genetic Algorithm

<p>Content-Based Image Retrieval (CBIR) aims at retrieving the images from the database based on the user query which is visual form rather than the traditional text form. The applications of CBIR extends from surveillance to remote sensing, medical imaging to weather forecasting, security systems to historical research and so on. Though extensive research is made on content based image retrieval in the spatial domain, we have most images in the internet which is JPEG compressed which pushes the need for image retrieval in the compressed domain itself rather than decoding it to raw format before comparison and retrieval. This research addresses the need to retrieve the images from the database based on the features extracted from the compressed domain along with the application of genetic algorithm in improving the retrieval results. The research focuses on various features and their levels of impact on improving the precision and recall parameters of the CBIR system. Our experimentation results also indicate that the CBIR features in compressed domain along with the genetic algorithm usage improves the results considerably when compared with the literature techniques.</p><p> </p>

A Novel Method for Satellite Image Retrieval using Semantic Mining and Hashing

Satellite images play an important role for collection of geographical information. However, the use of such images is limited to a greater extent due to its retrieval complexities. Traditional methods using text have also failed to yield desired and time-saving results. Therefore, Content based image retrieval using high semantic features has been developed to overcome problems related to text retrieval and challenges associated with semantic gap. Keeping in consideration the importance of semantic features for CBIR, the proposed method introduces a novel method using Semantic mining and Hashing for fast retrieval and attainment of accurate results. General Terms Remote sensing, Satellites, Data sets, Content based image retrieval (CBIR) and Algorithms.

Region-based image retrieval in the compressed domain using shape-adaptive DCT

Content-based image retrieval (CBIR) has drawn substantial research and many traditional CBIR systems search digital images in a large database based on features, such as color, texture and shape of a given query image. A majority of images are stored in compressed format and most of compression technologies adopt different kinds of transforms to achieve compression. Therefore, features can be extracted directly from images in compressed format by using, for example, discrete cosine transform (DCT) for JPEG compressed images. Region-based image retrieval (RBIR) is an image retrieval approach which focuses on contents from regions of images, instead of the content from the entire image in early CBIR. Although RBIR approaches attempt to solve the semantic gap problem existed in global low-level features in CBIR by using local low-level features based on regions of images. This paper proposes a new RBIR approach using Shape adaptive discrete cosine transform (SA-DCT). At a bottom level, local features are constructed from the coefficients of quantized block transforms (low-level features) for each region. Quantization acts for the concentration of block-wise information in a more condense way, which is highly desirable for the retrieval tasks. At an intermediate level, histograms of local image features are used as descriptors of statistical information. Finally, at the top level, the combination of histograms from different image regions (objects) is defined as a way to incorporate high-level semantic information. In this retrieval system, an image has a prior segmentation alpha plane, which is defined exactly as in MPEG-4. Therefore, an image is represented by segmented regions, each of which is associated with a feature vector derived from DCT and SA-DCT coefficients. Users can select any region as the main theme of the query image. The similarity between a query image and any database image is ranked according to a same similarity measure computed from the selected regions between two images. For those images without distinctive objects and scenes, users can still select the whole image as the query condition. The experimental results show that the proposed approach is able to identify main objects and reduce the influence of background in the image, and thus improve the performance of image retrieval in comparison with a conventional CBIR based on DCT.

K-means based histogram using multiresolution feature vectors for color texture database retrieval

Colorand texture are two important features in content-based image retrieval. It has been shown that using the combination of both could provide better performance. In this paper, a K-means based h...

Determination of Image Features for Content-based Image Retrieval using Interactive Genetic Algorithm

The development of content-based image retrieval (CBIR) system has become a significant research issue nowadays, as the digital image libraries and other multimedia databases are mounting very fast in the different areas. It is important to effectively and precisely retrieve the desired images from a large image database. Most of the approaches proposed are for finding the image features and some of the approaches include the user’s subjectivity and preferences in the image retrieval process. This paper explains mainly about the determination of the image features like color, texture, and edge for the content-based image retrieval system which uses the interactive genetic algorithm. The color feature is extracted by using mean and standard deviation, the texture feature is extracted by using gray level cooccurrence matrix (GLCM) and the edge features of an image are extracted by using the edge histogram descriptor (EHD). Here the term interactive genetic algorithm (IGA) helps to reach more close to the user’s need and satisfaction of image retrieval.