Improve Image Retrieval Performance Research Articles

Improving Image Retrieval Performance with SCS and MCS Clustering Techniques

Improving Image Retrieval Performance with SCS and MCS Clustering Techniques

Semantic Image Retrieval Analysis Based on Deep Learning and Singular Value Decomposition

The exponential growth in the total quantity of digital images has necessitated the development of systems that are capable of retrieving these images. Content-based image retrieval is a technique used to get images from a database. The user provides a query image, and the system retrieves those photos from the database that are most similar to the query image. The image retrieval problem pertains to the task of locating digital photographs inside extensive datasets. Image retrieval researchers are transitioning from the use of keywords to the utilization of low-level characteristics and semantic features. The push for semantic features arises from the issue of subjective and time-consuming keywords, as well as the limitation of low-level characteristics in capturing high-level concepts that users have in mind. The main goal of this study is to examine how convolutional neural networks can be used to acquire advanced visual features. These high-level feature descriptors have the potential to be the most effective compared to the handcrafted feature descriptors in terms of image representation, which would result in improved image retrieval performance. The (CBIR-VGGSVD) model is an ideal solution for content-based image retrieval that is based on the VGG-16 algorithm and uses the Singular Value Decomposition (SVD) technique. The suggested model incorporates the VGG-16 model for the purpose of extracting features from both the query images and the images kept in the database. Afterwards, the dimensionality of the features retrieved from the VGG-16 model is reduced using SVD. Then, we compare the query photographs to the dataset images using the cosine metric to see how similar they are. When all is said and done, images that share a high degree of similarity will be successfully extracted from the dataset. A validation of the retrieval performance of the CBIR-VGGSVD model is performed using the Corel-1K dataset. When the VGG-16 standard model is the sole one used, the implementation will produce an average precision of 0.864. On the other hand, when the CBIR-VGGSVD model is utilized, this average precision is revealed to be (0.948). The findings of the retrieval ensured that the CBIR-VGGSVD model provided an improvement in performance on the test pictures that were utilized, surpassing the performance of the most recent approaches.

IDHashGAN: Deep Hashing With Generative Adversarial Nets for Incomplete Data Retrieval

Benefiting from low storage costs and high retrieval efficiency, hash learning has been a widely adopted technology for approximating nearest neighbor in large-scale data retrieval. Deep learning to hash greatly improves image retrieval performance by integrating feature learning and hash coding into an end-to-end framework. However, subject to application scope, most existing deep hashing methods only apply to retrieval of complete data and have undesirable results when retrieving incomplete but valuable data. In this paper we propose IDHashGAN, a novel deep hashing model with generative adversarial networks to retrieve incomplete data, in which feature restoration, feature learning and hash coding are integrated into an unified end-to-end framework. The proposed model consists of four key components: (1) reconstructive and generative loss are used to generate continuous feature of incomplete data in generative network; (2) supervised manifold similarity is proposed to improve retrieval accuracy and obtain good user acceptance; (3) adversarial and classified loss are designed to distinguish authenticity and similarity in discriminative network; and (4) encoding and quantization loss are adopted to preserve similarity and control hash quality. Extensive experiments on benchmark datasets show that IDHashGAN is competitive on complete dataset and yields substantial boosts of 70% on incomplete datasets compared to state-of-the-art hashing methods.

Block-based pseudo-relevance feedback for image retrieval

ABSTRACT Pseudo-relevance feedback (PRF) is a relevance feedback (RF) technique for information retrieval that treats the top k retrieved images as relevance feedback. PRF is used to avoid the limitations of the traditional RF approach, which is a human-in-the-loop process. Although the pseudo-relevance feedback set contains noise, PRF can perform retrieval reasonably effectively. For implementing PRF, the Rocchio algorithm has been considered reasonably effective and is a well-established baseline method. However, it simply treats all of the top k feedback images as being equally similar to the query. Therefore, we present a block-based PRF approach for improving image retrieval performance. In this approach, images in the positive and negative feedback sets are further divided into predefined blocks, each of which contains one to several images, and blocks containing higher- or lower-ranked images will be assigned higher or lower weights, respectively. Experiments using the NUS-WIDE-LITE and Caltech 256 datasets and two different feature representations consistently show that the proposed approach using 30 blocks outperforms the baseline PRF in terms of P@10, P@20, and P@50. Furthermore, we show that a system that incorporates the user’s feedback allows the 30-block-based PRF approach to perform even better.

Object-Based Image Retrieval Using the U-Net-Based Neural Network.

Day by day, all the research communities have been focusing on digital image retrieval due to more internet and social media uses. In this paper, a U-Net-based neural network is proposed for the segmentation process and Haar DWT and lifting wavelet schemes are used for feature extraction in content-based image retrieval (CBIR). Haar wavelet is preferred as it is easy to understand, very simple to compute, and the fastest. The U-Net-based neural network (CNN) gives more accurate results than the existing methodology because deep learning techniques extract low-level and high-level features from the input image. For the evaluation process, two benchmark datasets are used, and the accuracy of the proposed method is 93.01% and 88.39% on Corel 1K and Corel 5K. U-Net is used for the segmentation purpose, and it reduces the dimension of the feature vector and feature extraction time by 5 seconds compared to the existing methods. According to the performance analysis, the proposed work has proven that U-Net improves image retrieval performance in terms of accuracy, precision, and recall on both the benchmark datasets.

Scene search based on the adapted triangular regions and soft clustering to improve the effectiveness of the visual-bag-of-words model

The storage size of the image and video repositories are growing day by day due to the extensive use of digital image acquisition devices. The position of an object within an image is obtained by analyzing the content-based properties like shape, texture, and color, while compositional properties present the image layout and include the photographic rule of composition. The high-quality images are captured on the basis of the rule of thirds that divide each image into nine square areas. According to this rule, salient objects of an image are placed on the intersection points or along the imagery lines of the grid to capture the position of the salient objects. To improve image retrieval performance, visual-bag-of-words (VBoW) framework-based image representation is widely used nowadays. According to this framework, the spatial relationship between salient objects of an image is lost due to the formation of a global histogram of the image. This article presents a novel adapted triangular area-based technique, which computes local intensity order pattern (LIOP) features, weighted soft codebooks, and triangular histograms from the four triangular areas of each image. The proposed technique adds the spatial contents from four adapted triangular areas of each image to the inverted index of the VBoW framework, solve overfitting problem of the larger sizes of the codebook, and overwhelmed the problem of the semantic gap. The experimental results and statistical analysis performed on five image collections show an encouraging robustness of the proposed technique that is compared with the recent CBIR techniques.

Scalable histopathological image analysis via supervised hashing with multiple features.

Histopathology is crucial to diagnosis of cancer, yet its interpretation is tedious and challenging. To facilitate this procedure, content-based image retrieval methods have been developed as case-based reasoning tools. Especially, with the rapid growth of digital histopathology, hashing-based retrieval approaches are gaining popularity due to their exceptional efficiency and scalability. Nevertheless, few hashing-based histopathological image analysis methods perform feature fusion, despite the fact that it is a common practice to improve image retrieval performance. In response, we exploit joint kernel-based supervised hashing (JKSH) to integrate complementary features in a hashing framework. Specifically, hashing functions are designed based on linearly combined kernel functions associated with individual features. Supervised information is incorporated to bridge the semantic gap between low-level features and high-level diagnosis. An alternating optimization method is utilized to learn the kernel combination and hashing functions. The obtained hashing functions compress multiple high-dimensional features into tens of binary bits, enabling fast retrieval from a large database. Our approach is extensively validated on 3121 breast-tissue histopathological images by distinguishing between actionable and benign cases. It achieves 88.1% retrieval precision and 91.3% classification accuracy within 16.5ms query time, comparing favorably with traditional methods.

Image Retrieval using Multi Cascaded Features

This paper depends on Content Based Image Retrieval CBIR to retrieve desired images from a large images database. It is based on extracting cascade features from image such as color, shape and texture features. The Euclidean Distance is used to measure similarity of each feature and retrieve similar images from the image database. The proposed method contains three stages: in the first stage, the HSV color histogram from query image is found and the nearest 100 images are retrieved. In the second stage the shape features are extracted from edge detection and the nearest 50 image are retrieved. In the last stage the texture feature are found based on first order features and the nearest 10 images are retrieved. In this paper a mechanism for image retrieval based on cascading approach is developed to improve image retrieval performance and reduce the computational time required to retrieve images. It is found that cascading features with Euclidean distance give about 76% precession of image retrieval.

Image retrieval based on color and motif characteristics

In order to improve image retrieval performance,this paper proposed a new image retrieval algorithm based on motif and color features.The color image edge gradient was detected,and by means of edge gradient image transform,a motif image was obtained.Adopting the gravity center of motif image as the datum point,the distances of all points were calculated to the datum point to get the motif center distance histogram.The all motifs of the motif image were projected in four different directions to get motif projective histogram.Color image was uniformly quantized into 64-color space from RGB space to obtain the color histogram.The above three histograms described image features for image retrieval.The experimental results show that the algorithm has high precision and recall.

Improved Image Retrieval Performance Based on Fuzzy C-means Clustering Algorithm

Improved Image Retrieval Performance Based on Fuzzy C-means Clustering Algorithm

Kernel method for improving image retrieval performance: a survey

The subject of this paper is the kernel method, which offers an elegant solution to improve the effectiveness and the efficiency of the learning process in an image retrieval system. By simulating learning in high-dimensional feature spaces while working with the original low-dimensional input data, kernel methods provide a way for obtaining non-linear decision boundaries from algorithms previously restricted to handling only linearly separable image collections. The aim of this paper is to summarise results that have been obtained by using the kernel method in image retrieval systems. First, the paper introduces the kernel method together with some of its useful properties, and several kernel types used in fields like image retrieval, text mining, machine learning, and computational biology. Then, the paper focuses on applications and research questions involving the kernel method, such as kernel-based learning, kernel selection and feature selection methods.

An effective image retrieval mechanism using family-based spatial consistency filtration with object region

How to construct an appropriate spatial consistent measurement is the key to improving image retrieval performance. To address this problem, this paper introduces a novel image retrieval mechanism based on the family filtration in object region. First, we supply an object region by selecting a rectangle in a query image such that system returns a ranked list of images that contain the same object, retrieved from the corpus based on 100 images, as a result of the first rank. To further improve retrieval performance, we add an efficient spatial consistency stage, which is named family-based spatial consistency filtration, to re-rank the results returned by the first rank. We elaborate the performance of the retrieval system by some experiments on the dataset selected from the key frames of “TREC Video Retrieval Evaluation 2005 (TRECVID2005)”. The results of experiments show that the retrieval mechanism proposed by us has vast major effect on the retrieval quality. The paper also verifies the stability of the retrieval mechanism by increasing the number of images from 100 to 2000 and realizes generalized retrieval with the object outside the dataset.

Distance-based Multiple Paths Quantization of Vocabulary Tree for Object and Scene Retrieval

The state of the art in image retrieval on large scale databases is achieved by the work inspired by the text retrieval approaches. A key step of these methods is the quantization stage which maps the high-dimensional feature vectors to discriminatory visual words. This paper mainly proposes a distance-based multiple paths quantization (DMPQ) algorithm to reduce the quantization loss of the vocabulary tree based methods. In addition, a more efficient way to build a vocabulary tree is presented by using sub-vectors of features. The algorithm is evaluated on both the benchmark object recognition and the location recognition databases. The experimental results have demonstrated that the proposed algorithm can effectively improve image retrieval performance of the vocabulary tree based methods on both the databases.

Improving image retrieval performance by inter-query learning with one-class support vector machines

Relevance feedback (RF) is an iterative process which improves the performance of content-based image retrieval by modifying the query and similarity metric based on the user’s feedback on the retrieval results. This short-term learning within a single query session is called intra-query learning. However, the interaction history of previous users over all past queries may also be potentially exploited to help improve the retrieval performance for the current query. The long-term learning accumulated over the course of many query sessions is called inter-query learning. We present a novel RF framework that learns one-class support vector machines (1SVM) from retrieval experience to represent the set memberships of users’ high-level concepts and stores them in a “concept database”. The “concept database” provides a mechanism for accumulating inter-query learning obtained from previous queries. By doing a fuzzy classification of a query into the regions of support represented by the 1SVMs, past experience is merged with current intra-query learning. The geometric view of 1SVM allows a straightforward interpretation of the density of past interaction in a local area of the feature space and thus allows the decision of exploiting past information only if enough past exploration of the local area has occurred. The proposed approach is evaluated on real data sets and compared against both traditional intra-query-learning-only RF approaches and other methods that also exploit inter-query learning.

Statistical correlation analysis in image retrieval

A statistical correlation model for image retrieval is proposed. This model captures the semantic relationships among images in a database from simple statistics of user-provided relevance feedback information. It is applied in the post-processing of image retrieval results such that more semantically related images are returned to the user. The algorithm is easy to implement and can be efficiently integrated into an image retrieval system to help improve the retrieval performance. Preliminary experimental results on a database of 100,000 images show that the proposed model could improve image retrieval performance for both content- and text-based queries.

Web mining for Web image retrieval

AbstractThe popularity of digital images is rapidly increasing due to improving digital imaging technologies and convenient availability facilitated by the Internet. However, how to find user‐intended images from the Internet is nontrivial. The main reason is that the Web images are usually not annotated using semantic descriptors. In this article, we present an effective approach to and a prototype system for image retrieval from the Internet using Web mining. The system can also serve as a Web image search engine. One of the key ideas in the approach is to extract the text information on the Web pages to semantically describe the images. The text description is then combined with other low‐level image features in the image similarity assessment. Another main contribution of this work is that we apply data mining on the log of users' feedback to improve image retrieval performance in three aspects. First, the accuracy of the document space model of image representation obtained from the Web pages is improved by removing clutter and irrelevant text information. Second, to construct the user space model of users' representation of images, which is then combined with the document space model to eliminate mismatch between the page author's expression and the user's understanding and expectation. Third, to discover the relationship between low‐level and high‐level features, which is extremely useful for assigning the low‐level features' weights in similarity assessment.

Web mining for Web image retrieval

The popularity of digital images is rapidly increasing due to improving digital imaging technologies and convenient availability facilitated by the Internet. However, how to find user-intended images from the Internet is nontrivial. The main reason is that the Web images are usually not annotated using semantic descriptors. In this article, we present an effective approach to and a prototype system for image retrieval from the Internet using Web mining. The system can also serve as a Web image search engine. One of the key ideas in the approach is to extract the text information on the Web pages to semantically describe the images. The text description is then combined with other low-level image features in the image similarity assessment. Another main contribution of this work is that we apply data mining on the log of users' feedback to improve image retrieval performance in three aspects. First, the accuracy of the document space model of image representation obtained from the Web pages is improved by removing clutter and irrelevant text information. Second, to construct the user space model of users' representation of images, which is then combined with the document space model to eliminate mismatch between the page author's expression and the user's understanding and expectation. Third, to discover the relationship between low-level and high-level features, which is extremely useful for assigning the low-level features' weights in similarity assessment.