Image Similarity Learning through Rankboost Mechanism Based On Keywords and Content Queries

Thangka is integral part of Tibetan painting art, is also a bright pearl in the treasure house of world culture as well as a valuable cultural heritage of the world. Thangka's digital works are meaningful and valuable, since in today's fast-growing information age, multimedia data is becoming more and more common in people's daily applications, however this data is nearly useless if there is no computer-aided browsing, searching, and retrieving mechanism to obtain the desired contents. And it involves many research content such as database management system, feature extraction and presentation, content-based image retrieval, semantic-based image retrieval and search engines of Thangka image and so on. In this paper, the characteristics of Thangka image, content-based image retrieval (CBIR) system for Thangka is introduced, and a semantic-based image retrieval and multimedia presentation management system model for Thangka image is also proposed.

In recent years, secure image archival and retrieval model in cloud computing (CC) received significant attention to ensure data confidentiality and secured data transmission among cloud server and clients cloud storage and users. Traditional secure image retrieval (IR) techniques are not applicable to adapt with a large-scale IR in cloud environment. In order to overcome these issues, this article introduces an effective secure IR scheme using Inception with ResNet v2 (SIRS-IR) and Multiple Share Creation (MSC) scheme. The proposed method uses Inception with ResNet v2 model based feature extraction process. At the same time, the MSC process is utilized for multiple share generation and then encryption of shares takes place using double chaotic logistic map (DLCM) technique. Besides, the encrypted shares and the respective feature vectors are saved in the cloud server with the corresponding image identification number. During the IR process for the applied QI, the SIRS-IR model extracts the feature vectors and performs similarity measurement to retrieve the related images from the database interms of encrypted shares. Finally, the share decryption process is carried out for the reconstruction of original images with no loss of quality. Extensive experimentations were performed to verify the retrieval performance and image quality of the reconstructed images using Corel10K dataset. The obtained results stated that the presented SIRS-IR model is found to be superior to other methods in a considerable way.

In content-based image retrieval (CBIR), one of the most challenging and ambiguous tasks are to correctly understand the human query intention and measure its semantic relevance with images in the database. Due to the impressive capability of visual saliency in predicting human visual attention that is closely related to the query intention, this paper attempts to explicitly discover the essential effect of visual saliency in CBIR via qualitative and quantitative experiments. Toward this end, we first generate the fixation density maps of images from a widely used CBIR dataset by using an eye-tracking apparatus. These ground-truth saliency maps are then used to measure the influence of visual saliency to the task of CBIR by exploring several probable ways of incorporating such saliency cues into the retrieval process. We find that visual saliency is indeed beneficial to the CBIR task, and the best saliency involving scheme is possibly different for different image retrieval models. Inspired by the findings, this paper presents two-stream attentive CNNs with saliency embedded inside for CBIR. The proposed network has two streams that simultaneously handle two tasks. The main stream focuses on extracting discriminative visual features that are tightly related to semantic attributes. Meanwhile, the auxiliary stream aims to facilitate the main stream by redirecting the feature extraction to the salient image content that human may pay attention to. By fusing these two streams into the Main and Auxiliary CNNs (MAC), image similarity can be computed as the human being does by reserving conspicuous content and suppressing irrelevant regions. Extensive experiments show that the proposed model achieves impressive performance in image retrieval on four public datasets.

The purpose of dissertation is to utilize connotative messages for enhancing image retrieval and browsing. By adopting semiotics as a theoretical tool, this study explores problems of image retrieval and proposes an image retrieval model. The semiotics approach conceptually demonstrates that: 1) a fundamental reason for the dissonance between retrieved images and user needs is representation of connotative messages, and 2) the image retrieval model which makes use of denotative index terms is able to facilitate users to browse connotatively related images effectively even when the users' needs are potentially expressed in the form of denotative query. Two experiments are performed for verifying the semiotic-based image retrieval model and evaluating the effectiveness of the model. As data sources, 5,199 records are collected from Artefacts Canada: Humanities by Canadian Heritage Information Network, and the candidate terms of connotation and denotation are extracted from Art & Architecture Thesaurus. The first experiment, by applying term association measures, verifies that the connotative messages of an image can be derived from denotative messages of the image. The second experiment reveals that the association thesaurus which is constructed based on the associations between connotation and denotation facilitates assigning connotative terms to image documents. In addition, the result of relevant judgments presents that the association thesaurus improves the relative recall of retrieved image documents as well as the relative recall of browsing sets. This study concludes that the association thesaurus indicating associations between connotation and denotation is able to improve the accessibility of the connotative messages. The results of the study are hoped to contribute to the conceptual knowledge of image retrieval by providing understandings of connotative messages within an image and to the practical design of image retrieval system by proposing an association thesaurus which can supplement the limitations of the current content-based image retrieval systems (CBIR).

Online image repositories such as Flickr contain hundreds of millions of images and are growing quickly. Along with that the needs for supporting indexing, searching and browsing is becoming more and more pressing. In this work we will employ the image content as a source of information to retrieve images. We study the representation of images by Latent Dirichlet Allocation (LDA) models for content-based image retrieval. Image representations are learned in an unsupervised fashion, and each image is modeled as the mixture of topics/object parts depicted in the image. This allows us to put images into subspaces for higher-level reasoning which in turn can be used to find similar images. Different similarity measures based on the described image representation are studied. The presented approach is evaluated on a real world image database consisting of more than 246,000 images and compared to image models based on probabilistic Latent Semantic Analysis (pLSA). Results show the suitability of the approach for large-scale databases. Finally we incorporate active learning with user relevance feedback in our framework, which further boosts the retrieval performance.

A significant amount of multimedia data consists of digital images, and multimedia content analysis is used in many real-world computer vision applications. Multimedia information, especially photos, has become much more complicated in the last several years. Every day, millions of photos are posted to various websites, such as Instagram, Facebook, and Twitter. Finding a suitable image in an archive is a difficult research subject for the field of computer vision. Most search engines use standard text-based techniques that depend on metadata and captions in order to fetch photos. Over the past 20 years, a great deal of research has been conducted on content-based image retrieval (CBIR), picture categorization, and analysis. In image classification models and CBIR, high-level picture representations are represented as feature vectors made up of numerical values. Empirical evidence indicates a considerable disparity between picture feature representation and human visual understanding. Reducing the semantic gap between human visual understanding and picture feature representation is the aim of this study. This study aims to do a thorough analysis of the latest advancements in the domains of Content-Based picture Retrieval and picture representation. We performed a comprehensive analysis of many models for image retrieval and picture representation, encompassing the most recent advancements in semantic deep-learning methods and feature extraction. This paper provides an in-depth analysis of the key ideas and important studies related to image representation and content-based picture retrieval. In an effort to stimulate more research in this field, it also offers a preview of potential future study topics.

As the use of digital media increases, effective retrieval and management techniques become more important. In order to extract useful information from this huge amount of data, many content based image retrieval (CBIR) systems have been developed. A typical CBIR system captures image features such as color, texture, or shape of objects in the query image and tries to retrieve images from the database with similar features. The main advantage of CBIR systems with relevance feedback is that these systems take into account the gap between the high-level concepts and low-level features and subjectivity of human perception of visual content. In this paper, a CBIR system based on color histogram has been presented and a new approach for image storage and retrieval called association-based image retrieval (ABIR) has been studied. The results of both systems have been shown.

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.

One of the main challenges in medical image retrieval is the increasing volume of image data, which render it difficult for domain experts to find relevant information from large data sets. Effective and efficient medical image retrieval systems are required to better manage medical image information. Text‐based image retrieval (TBIR) was very successful in retrieving images with textual descriptions. Several TBIR approaches rely on models based on bag‐of‐words approaches, in which the image retrieval problem turns into one of standard text‐based information retrieval; where the meanings and values of specific medical entities in the text and metadata are ignored in the image representation and retrieval process. However, we believe that TBIR should extract specific medical entities and terms and then exploit these elements to achieve better image retrieval results. Therefore, we propose a novel reranking method based on medical‐image‐dependent features. These features are manually selected by a medical expert from imaging modalities and medical terminology. First, we represent queries and images using only medical‐image‐dependent features such as image modality and image scale. Second, we exploit the defined features in a new reranking method for medical image retrieval. Our motivation is the large influence of image modality in medical image retrieval and its impact on image‐relevance scores. To evaluate our approach, we performed a series of experiments on the medical ImageCLEF data sets from 2009 to 2013. The BM25 model, a language model, and an image‐relevance feedback model are used as baselines to evaluate our approach. The experimental results show that compared to the BM25 model, the proposed model significantly enhances image retrieval performance. We also compared our approach with other state‐of‐the‐art approaches and show that our approach performs comparably to those of the top three runs in the official ImageCLEF competition.

Content-based image retrieval (CBIR) has certain advantages over those pure keyword-based. CBIR indexes images by visual features that are extracted from the images. This may save the effort spent on the manual annotation. However, because low-level visual features, such as colour and texture, often carry no high-level concepts, images retrieved purely based on content may not match with the intention of the user. The work presented in this paper is an image retrieval system that bases both on text annotations and visual contents. It indexes and retrieves images by both keywords and visual features, with the purpose that the keywords may mend the gap between the semantic meaning an image carries and its visual content. Tests were made on the system that have demonstrated that such a hybrid approach did improve retrieval precisions over those pure content-based.

The complexity of multimedia content, particularly images, has risen dramatically in recent years, and millions of images are shared on social media every day. Finding or retrieving an appropriate image is becoming more difficult due to the increase in the volume of shared and archived multimedia data. Any image retrieval model must, at a bare minimum, locate and classify images that are visually related to the user’s query. The vast majority of Internet search engines employ text algorithms that fetch images using captions as input. Even though there is a lot of study being done to increase the effectiveness of automatic image annotation, retrieval errors can occur due to differences in visual perception. Content-based image retrieval (CBIR) addresses the aforementioned issue because visual analysis of the content is included in the query image. On the other hand, feature extraction is significantly challenging because of semantic gap. This work proposes a strategy for effective retrieval in similarity images using the triadic color scheme RGB, YCbCr, and L ∗ a ∗ b ∗ based on reranking. We want to increase image similarity and encourage more relevant reranking. As a result of the findings, it can be concluded that a triadic color scheme improves precision by 5% more dramatically than existing schemes and also efficiently improves retrieved results while reducing user effort.

Image retrieval is a critical task in many fields such as indexing and Content-Based Image Retrieval is a tried and tested method for image retrieval. With advances in graphics and visualization techniques, image retrieval should be easy, quick and flexible. The aim of this work is to develop an efficient image indexing and retrieval system to collate similar images or images based on conditioned queries in the form of an image itself. The indexing method has been performed using wavelet transform and related mechanisms. Its detailing can easily entail description from a broad range to a narrow range. The indexing algorithm applies a Haar Wavelet-Based Perceptual Similarity Index (HaarPSI) for Image Quality Assessment. The HaarPSI utilizes the coefficients obtained from a Haar wavelet decomposition to assess local similarities between two images, as well as the relative importance of image areas. Results show that the algorithm used is able to retrieve and display images with high retrieval accuracy and medium relevance.The proposed methodology has been tested against known image retrieval techniques and was found to be comparable and in some cases better than most prior known techniques. The testing was performed on images of varied types, from multiple datasets and sizes. The accuracy was found to be 99% in most cases. All in all, this study introduces a simple and convenient way of offline image searches on desktop computers and provides a stepping stone to future content-based image retrieval systems built for similar purposes.

Challenges faced by prevailing text metadata paradigms for online image search have inspired overwhelming research in Content Based Image Retrieval (CBIR). A multitude of approaches have been introduced within the literature, yet relatively few image search engines have been made publicly available on the web. Aside from challenges facing the user, such as describing a visual query using keywords, or finding an appropriate example image to initiate a visual search, all systems must inevitably grapple with the sensory and semantic gaps [Smeulders et al. 2000], which essentially represent a loss of information in the abstraction process. In this work, we challenge commonly suggested approaches to improving CBIR and illustrate drawbacks of relying on textual data, as well as visual data, in general CBIR search. We provide cogent examples using online visual search engines Behold™, Tiltomo Beta, Pixilimar, and Riya™ Beta. These examples demonstrate the effect of semantic ambiguities in natural language, which extend to search terms and text tags.

There is a growing trend for using content‐based image retrieval (CBIR) systems these days because of the constantly growing interest in digital content. Therefore, the ability of the CBIR to perform the CBIR process will depend on the feature extraction process and its basis, for the retrieval will be done on. Numerous researchers put forward various techniques for feature extraction to enhance the nature of the system. Since features play a very key role in enhancing performance, various features can be used collectively to attain the requisite goal. To retain this in mind, we present in this paper a multifeature fusion system, where three features are integrated and form one feature to improve the situation of retrieval. For this purpose, scale‐invariant feature transform (SIFT), speeded‐up robust features (SURF), and histogram of oriented gradients (HOG) features are adopted. These features are common features that deliver information about the shape of the object and for matching purposes, two techniques of distance matching such as Euclidean and Hausdrauff distance are adopted. To assess the performance of the proposed multifeature‐based CBIR approach, experiments were conducted with the usage of a MATLAB simulator. The Corel‐1000 dataset, consisting of 10,000 images in 100 semantic classes, turned into applied, with each magnificence containing 100 images. A subset of 2500 images across 50 semantic classes was used to train the system. This research aligns with industry, innovation, and infrastructure by contributing to advancements in image processing and retrieval systems. Key characteristic descriptors, along with SIFT, SURF, HOG, texture, and multicharacteristic combinations, were extracted for retrieval functions. The results display that the usage of the Hausdrauff distance as a similarity degree outperforms Euclidean distance, accomplishing retrieval accuracies of 80.02% for HOG, 77.9% for SIFT, 79. 8% for SURF, 77.2% for texture, and 84.2% for multicharacteristic combinations, surpassing Euclidean distance results via 1.7%–3.6% across capabilities. These findings underscore the effectiveness of Hausdrauff distance in enhancing retrieval precision within the CBIR framework.

Cyber-Physical System (CPS) is used in industries and automated plants as they have modern administration abilities and are capable of performing real-time processing in a distributed architecture. In mechanical plants, they are expected to perform image recognition and retrieval, and other Machine Learning (ML) processes for efficient functioning and screening of assembling units and products. The existing strategies like remote sensing retrieval and the Gaussian mixture model for Image Retrieval (IR) are not suitable for huge datasets. The drawbacks of the existing strategies can be eliminated based on features of data by using Improvised Density Scale-Invariant Feature Transform (ID-SIFT). This approach mainly focuses on geometrical features like color, texture, size and shape-based image matching offering an improved accuracy of 89%. This approach is relatively efficient when compared to existing algorithms used in IR.