Feature Based Image Retrieval Research Articles

An Image-Retrieval Method Based on Cross-Hardware Platform Features

Artificial intelligence (AI) models have already achieved great success in fields such as computer vision and natural language processing. However, deploying AI models based on heterogeneous hardware is difficult to ensure accuracy consistency, especially for precision sensitive feature-based image retrieval. In this article, we realize an image-retrieval method based on cross-hardware platform features, aiming to prove that the features of heterogeneous hardware platforms can be mixed, in which the Huawei Atlas 300V and NVIDIA TeslaT4 are used for experiments. First, we compared the decoding differences of heterogeneous hardware, and used CPU software decoding to help hardware decoding improve the decoding success rate. Then, we compared the difference between the Atlas 300V and TeslaT4 chip architectures and tested the differences between the two platform features by calculating feature similarity. In addition, the scaling mode in the pre-processing process was also compared to further analyze the factors affecting feature consistency. Next, the consistency of capture and correlation based on video structure were verified. Finally, the experimental results reveal that the feature results from the TeslaT4 and Atlas 300V can be mixed for image retrieval based on cross-hardware platform features. Consequently, cross-platform image retrieval with low error is realized. Specifically, compared with the Atlas 300V hard and CPU soft decoding, the TeslaT4 hard decoded more than 99% of the image with a decoding pixel maximum difference of +1/−1. From the average of feature similarity, the feature similarity between the Atlas 300V and TeslaT4 exceeds 99%. The difference between the TeslaT4 and Atlas 300V in recall and mAP in feature retrieval is less than 0.1%.

Deep CNN Based Hybrid Model for Image Retrieval

The popularity of deep features based image retrieval and classification task has grown a lot in the recent years. Feature representation based on Convolutional Neural Networks (CNNs) found to be very effective in terms of accuracy by various researchers in the field of visual content based image retrieval. The features which are neutral to their domain knowledge with automatic learning capability from their images are in demand in various image applications. For improving accuracy and expressive power, pre-trained CNN models with the use of transfer learning can be utilized by training them on huge volume of datasets. In this paper, a hybrid model for image retrieval is being proposed by using pre-trained values of hyper parameters as input learning parameters. The performance of the model is being compared with existing pre-trained models showing higher performance on precision and recall parameters

Texture Image Classification and Retrieval Using Multi-resolution Radial Gradient Binary Pattern

ABSTRACT In image classification and retrieval applications, images are represented by a set of features. These features are extracted from both spatial and wavelet transformed input images. The wavelet transform decomposes images into multiple resolutions by separating smooth and sharp information in individual channels to give more details about the image. Each level of smooth and sharp decomposed channels is individually involved in texture feature extraction. It is difficult to achieve better results in image classification and retrieval applications with fewer discriminant details of the image. Even though each level of the smooth channel correlates with its successive decomposition levels, these feature extraction methods do not consider the relationship between the multiple levels of decomposed images. This motivates the proposed work to extract the correlation between the different levels of wavelet decomposed images. The proposed work (i) encodes the local difference obtained from multiple radii across the different levels of wavelet decomposed channels, (ii) assesses the proposed texture feature extraction method using classification and retrieval experiments with five different wavelet filters over twelve image databases and (iii) analyses the importance of similarity measures involved in the proposed feature-based image retrieval experiments.

Combined global and local semantic feature–based image retrieval analysis with interactive feedback

Nowadays, user expects image retrieval systems using a large database as an active research area for the investigators. Generally, content-based image retrieval system retrieves the images based on the low-level features, high-level features, or the combination of both. Content-based image retrieval results can be improved by considering various features like directionality, contrast, coarseness, busyness, local binary pattern, and local tetra pattern with modified binary wavelet transform. In this research work, appropriate features are identified, applied and results are validated against existing systems. Modified binary wavelet transform is a modified form of binary wavelet transform and this methodology produced more similar retrieval images. The proposed system also combines the interactive feedback to retrieve the user expected results by addressing the issues of semantic gap. The quantitative evaluations such as average retrieval rate, false image acceptation ratio, and false image rejection ratio are evaluated to ensure the user expected results of the system. In addition to that, precision and recall are evaluated from the proposed system against the existing system results. When compared with the existing content-based image retrieval methods, the proposed approach provides better retrieval accuracy.

Determination of Color and Shape Features Based Image Retrieval with Hybrid Feature Descriptor Construction (HFDC)

Objectives: To implement a hybrid feature descriptor construction (HFDC) model to exercise the CBIR. Method/analysis: The model that combines color and shape features of visual content to produce a feature level fusion scheme is introduced. The RGB histogram, HSV histogram and the canny edge histogram features are extracted and fused to produce a hybrid feature vector. Then the determined feature vector through the fusion of the entire dataset is used to train the SVM using RBF kernel to retrieve relevant visual content through identifying the color distribution, and shape is focused here as the main objective. Since from the very beginning of the data usage to surf web information the classification of the similar related objects has been potentially provided a helpful contribution towards helping the users to identify and determine required knowledge from the large corpus of available digital information. Many algorithms and improvements have been in implementation, but the large quantity of available information provides complexity to these techniques to enhance computational hike. This feature level fusion contributes to reducing the overhead. Finding: The HFDC evolutions significantly contribute to achieving an accuracy of 84.60%. The experimental results have proven the efficiency of the HFDC by providing the maximum classification accuracy. Novelty: The results evidently show that (1) HDFC improve the performance by enhancing the feature level fusion process, (2) the fusion procedure produces increased solid and high indicative rendering and (3) by performing feature level fusion of data a core dictionary is provided for better CBIR performance. Improvement: In addition to color and shape feature fusion the texture features can also be combined to improve the performance significantly.Keywords: CBIR, HFDC, RGB, HSV, Canny

Content based Retrieval Management Systems in Web Engineering

The tremendous upsurge and abundant availability of graphic documents on the internet directed to the high concern in research on Content-Based Image Retrieval (CBIR). It has cemented the attitude for a massive sum of innovative procedures and schemes, and growing curiosity in respective fields to preserve such research. Existing technologies were discussed about various CBIR techniques such as k-means, content-based image, and hybrid clustering, etc. in combination with the feature vector of information images and texture features. In similar cases, it is constricted in stating the user’s semantic knowledge to permit information distribution and reuse. Hence models ought to be managed within repositories, where they might be retrieved upon users’ queries. Due to the lack of adequate tools on incisive/handling for visual content, this research work proposes an Efficient Density-based Clustering Algorithm (EDBC) for CBIR technique that will enhance scalability, reduce the user search time, and lower the maintenance cost. Using the existing CBIR, the color, texture, and shape features of an image is identified by integrating with the proposed EDBC algorithm and thereby, it improves the scalability and user search time. When comparing the feature between the color histograms of RGB and CMYK, it shows better color characteristics in CMYK by using the proposed technique. Also, the grouping of color, shape and texture features based image retrieval improves the accuracy when compared with existing methods

Geometry and Topology Preserving Hashing for SIFT Feature

In recent years, content-based image retrieval has been of concern because of practical needs on Internet services, especially methods that can improve retrieving speed and accuracy. The SIFT feature is a well-designed local feature. It has mature applications in feature matching and retrieval, whereas the raw SIFT feature is high dimensional, with high storage cost as well as computational cost in feature similarity measurements. Thus, we propose a hashing scheme for fast SIFT feature-based image matching and retrieval. First, a training process of the hashing function involves geometric and topological information being introduced; second, a geometry-enhanced similarity evaluation that considers both the global and details of images in evaluation is explained. Compared with state-of-the-art methods, our method achieves better performance.

Diversified textual features based image retrieval

How to target the users’ demands faster and more accurately has become a hot issue in the domain of image retrieval. Most of the existing techniques focus on retrieving the most relevant images to the query, which will reduce the search efficiency and make the retrieval process boring for users. For these reasons, diversity-induced image retrieval is proposed to guarantee that the retrieval results are not only relevant to the query, but also cover the aspects of the query as many as possible. Most of the traditional diversity-induced methods seldom use the multi-modal information to compensate each other. Moreover, the traditional textual features come from the complete textual information which can not highlight the unique property of an image. In this paper, we propose a new retrieval framework that combines visual and textual information in an effective way. Firstly, Latent Dirichlet Allocation (LDA) is applied on visual features to construct topics. Then textual information is used to improve the coherence of the topics. To extract diversified textual features, we propose a new method focusing on the unique words. For topic improvement, we design a new strategy that removes outliers from the constructed graph. Finally, the representative images are selected as retrieval results. The experiments on the database of MediaEval 2013 prove the superiority of our method. Besides, we also illustrate the results of diversified textual feature extraction and similarity graph clustering, which indicates the effectiveness of the proposed method.

An efficient image retrieval framework using fused information feature

The tremendous growth in the number of digital images has provoked the need for improvement in search and retrieval of images from a large database. The biggest challenge faced in this process is retrieval of the required images from a large database with maximum precision and minimum retrieval time. Selecting appropriate features plays a significant role in improving the performance of image retrieval systems. In this paper, Fused Information Feature-based Image Retrieval System (FIF-IRS) is proposed, consisting of 8-Directional Gray Level Co-occurrence Matrix (8D-GLCM) and HSV Color Moments (HSVCM) features. The performance of the image retrieval system is evaluated by the metrics Precision, Error Rate, and Retrieval Time. The proposed image retrieval system is tested on benchmark image databases- Corel-1K, Corel-5K, and Corel-10K. The experimental results obtained prove that there is a significant improvement while applying the proposed method when compared to existing state-of-the-art techniques with attained precision of 83.3%, 66.9%, and 56.4% on the three databases respectively.

Fine-Grained Classification of Product Images Based on Convolutional Neural Networks

With the rapid development of the Internet of things and e-commerce, feature-based image retrieval and classification have become a serious challenge for shoppers searching websites for relevant product information. The last decade has witnessed great interest in research on content-based feature extraction techniques. Moreover, semantic attributes cannot fully express the rich image information. This paper designs and trains a deep convolutional neural network that the convolution kernel size and the order of network connection are based on the high efficiency of the filter capacity and coverage. To solve the problem of long training time and high resource share of deep convolutional neural network, this paper designed a shallow convolutional neural network to achieve the similar classification accuracy. The deep and shallow convolutional neural networks have data pre-processing, feature extraction and softmax classification. To evaluate the classification performance of the network, experiments were conducted using a public database Caltech256 and a homemade product image database containing 15 species of garment and 5 species of shoes on a total of 20,000 color images from shopping websites. Compared with the classification accuracy of combining content-based feature extraction techniques with traditional support vector machine techniques from 76.3% to 86.2%, the deep convolutional neural network obtains an impressive state-of-the-art classification accuracy of 92.1%, and the shallow convolutional neural network reached a classification accuracy of 90.6%. Moreover, the proposed convolutional neural networks can be integrated and implemented in other colour image database.

Topological map construction and scene recognition for vehicle localization

This paper presents a vehicle localization method to assist vehicle navigation based on topological map construction and scene recognition. A topological map is constructed using omni-directional image sequences, and the node information of the topological map is used for place recognition and derivation of vehicle location. In topological map construction and scene change detection, we utilize the Extended-HCT method for semantic description and feature extraction. Content-based and feature-based image retrieval approaches are adopted for place recognition and vehicle localization on the real scene image dataset. The proposed technique is able to construct a real-time image retrieval system for navigation assistance and validate the correctness of the route. Experiments are carried out in both the indoor and outdoor environments using real world images.

Spatial Features Analysis of Automatically Annotated Images

In this paper, a new method for spatial features based image retrieval is proposed. These are the features specifying location of an image segment in an image and relative spatial location between two image segments etc. Spatial feature adds extra capability of the system in addition to texture, color and shape based object recognition system. Heuristic and semantic features of the image segments are identified from low level color, texture descriptors and directly stored into the image database and easily retrieved also. Position of image segments in the image is detected and stored in the image database that will be accessed for retrieval process later. Object ontology is used to determine the spatial relation between heuristics. These spatial relations and corresponding concept are identified and stored. Spatial rank between two segments will be calculated and stored along with segment ids. Spatial rank is calculated by a new technique. Proper concepts are associated with image segment based on ontology based spatial relation.

Image Retrieval Process Based on Relevance Feedback and Ontology Using Decision Tree

In this paper, another strategy for immediate features based image recovery is proposed. Image database is developed with low level texture features got from Gray Level Co- Occurrence Matrix (GLCM) and measurable techniques for Tamura. Semantic level inquiries from the user mapped to the low level peculiarities at recovery time to recover the required images. Images with more than one moderate features can be recovered by utilizing intersection of images recovered by each of the queried feature. Artificial Neural Network (ANN) is utilized as a part of the following steps in the wake of accepting user inputs. In spite of the fact that semantics are utilized as search key as a part of the beginning steps, low level features are utilized as a part of the ANN based searching in later steps. Back propagation Algorithm is utilized as a part of learning step. This ANN based relevance feedback technique enhances accuracy of immediate feature based image retrieval method. Decision tree (DT) can likewise be connected in relevance feedback stage. Decision tree is framed in training stage and images will be tested by of the decision tree. Relation storing ontology related information is utilized as a part of every phase of retrieval procedure to evacuate ambiguities identified with synonyms and hypernym-homonym sets.

Secure Similarity Search over Encrypted Cloud Images

With the growing popularity of cloud computing, more and more data owners are willing to outsource their data to the cloud. However, private data should be encrypted before outsourcing for security requirements, which obsoletes data utilization like content-based image retrieval. In this paper, we propose a secure similarity image search scheme, which allows data owners to outsource their encrypted image database to the cloud server without revealing the real content of images. The proposed scheme supports both global and local feature based image retrieval under various distance metrics, such as earth mover's distance. Firstly, the data owner extracts either global features or local features from images to represent the images. Then, these features are used to generate a searchable index. Finally, both image database and searchable index are encrypted before outsourcing to the cloud server. When a query image coming, the data user extracts feature from the query image and generates the search trapdoor. The trapdoor is then sent to the cloud server and used to compare the similarity with the searchable index. Extensive experiments are conducted to show the efficiency and applicability of our proposed similarity image search system.

Semantic Concept and Weighted Visual Feature Based Image Retrieval

The performance of content-based image retrieval is degraded because of the existence of the semantic gap. In order to address this drawback, this paper proposes an image retrieval method fusing with the semantic concept and the weighted visual feature. In the approach, each image is segmented using the normalized cut (N-cut), and the visual characteristics are extracted. After that, the semantic concept is achieved based on the mapping from keywords to image low-level characteristics. The distinctive proportion of the same concept in different images may lead to the priority retrieval of the image which has the same similarity and smaller correlated regions with others. Therefore, we use the weighted visual features to sort the retrieved images. Extensive experiments show the retrieval performance of the proposed method is superior to the traditional content-based image retrieval methods.

Retrieval Model Based on Image Content Features

This paper presents fuzzy color histogram feature-based image retrieval method and texture spectrum fuzzy histogram feature analyzes the image database indexing techniques and the introduction of the experimental system for an improved method of fuzzy indexes. Algorithm reflects the underlying characteristics of high-level concepts and integration, relevance feedback and machine learning mechanism combining ideas. In this paper, the algorithm full processing power of computer systems, has a certain reference value and practical significance.

Feature based Image retrieval based on clustering, classification techniques using low level image features

Feature based Image retrieval based on clustering, classification techniques using low level image features

Heuristic and intermediate features based image retrieval

Heuristic and intermediate features based image retrieval

Color and Texture Features Based Image Retrieval

We propose a practical image retrieval scheme to retrieve images efficiently. We propose a scheme using color and texture features and address the unique algorithm to extract the color pixel features by the HSV color space and Tamura features of the texture features. The proposed scheme transfers each image to a quantized color code using the regulations of the properties in compliance with HSV color space model and then employing the quantized color code along with Tamura features of texture features to compare the images of database. Experimental of the proposed scheme on demonstrate more efficient and effective than the conventional schemes.

Feature Based Image Retrieval Using Fused Sift and Surf Features

In Computer Vision, the content based image retrieval (CBIR) is an emerging technique applied for image retrieval and also used in most of the search engines. In any feature based image retrieval methods, the extracted features and feature vectors are the essential key for image retrieval. The scale invariant feature transform (SIFT) and the speeded up robust features (SURF) are well-proven methods to extract features from images and those features can also be applied for image retrieval. But both of these methods have their own advantages and disadvantages when applied to image retrieval. By fusing the features extracted by SIFT and SURF, the proposed method forms a new feature vectors by computing histograms for the same and pass it to support vector machine (SVM) classifier for training. The K-means and k-d tree algorithms are also used for optimizing the retrieval results. The results of the parent methods and the proposed method are tabulated and compared by keeping the affecting parameters as same and also taking into account the anomalies. The proposed method is seen to give the best retrieval results in all the cases.