Personalized Content-Based Image Retrieval

Content Based Image Retrieval (CBIR) depends on the visual content of an image for obtaining similar images. Research on CBIR faces a challenge of semantic gap between low level features and high level visual content. The optimal solution must reduce the computational complexity and computational time in retrieval process. Relevance Feedback is a solution to reduce the semantic gap. A new framework for content based image retrieval is proposed which is Bipartite Reiterative algorithm. This gives an approach of iteration to relevance feedback mechanism. It consists of two levels which are basic retrieval scheme and reiterative feedback scheme. Positive images are obtained from the user as part of the basic retrieval scheme. New feedback algorithm is developed based on weights assigned to visual attributes. The weights are inversely proportional to the visual attribute distance between query image and the positive images given by the user. Iterative feedback query can be performed after the user feedback. The query will be progressed according to the user feedback. The algorithm reduces the computational complexity and computational time in image retrieval.

With the emerge of multimedia, communication and processing, a typical image database has a large volume of data. It is an essential to build an efficient retrieval system to browse through the entire database. In this paper we proposed precise Relevance Feed Back (RFB) Content Based Image Retrieval (CBIR) using multiple features based on interactive retrieval approach which will extensively reduces the semantic gap between low-level features and high-level semantics. Relevance Feed Back improves the retrieval accuracy of Content Based Image Retrieval(CBIR) by modifying the query based on the user's feedback in which the user can select the most relevant images and provide a weight of preference for each relevant image. In our approach we extracted multiple features by integrating color, texture and shape features with Relevance Feed Back. We extracted color features by cumulative histogram, texture feature are exploited by using Color co-occurrence matrix (CCM) and we describe the shape information contained in an image on the basis of its significant edges. We used Euclidean distance as the similarity measure. In our experiment we used Corel real-world image databases with 1000 images, divided into 10 categories, each category containing 100 images including landscapes, animals, plants, monuments, transport (cars, planes). Our results reflects that integrating color, texture and shape for retrieving similar images improves the retrieval performance with respect to retrieval accuracy and recall rate as compared with the classical color feature. During the experiment we also observed that incorporating Relevance Feed Back (RFB) with multiple features outperforms in comparison with multifeature vector without Relevance Feed Back method.

Image retrieval is one of the most interesting and fastest growing research areas in all fields. It is an effective and efficient tool for managing large image databases. In most Content-Based Image Retrieval (CBIR) systems, an image is represented by a set of low-level visual features; hence a direct correlation with high-level semantic information will be absent. Therefore, a gap exists between high-level information and low-level features, which is the main reason that hinders the improvement of the image retrieval accuracy. In this work, main focus is on the semantic based image retrieval system using Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction. Based on the texture features, semantic interpretations are given to the extracted textures. The images are retrieved according to user satisfaction and thereby reduce the semantic gap between low level features and high level features.

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) has suffered from the lack of linkage between low-level features and high-level semantics. Although relevance feedback (RF) CBIR provides a promising solution involving human interaction, certain query images poorly represented by low-level features still have unsatisfactory retrieval results. An innovative method has been proposed to increase the percentage of relevance of target image database by using graph cuts theory with the maximum-flow/minimum-cut algorithm and relevance feedback. As a result, the database is reformed by keeping relevant images while discarding irrelevant images. The relevance is increased and thus during following RF-CBIR process, previously poorly represented relevant images have higher probability to appear for selection. Better performance and retrieval results can thus be achieved.

The goal of content based image retrieval is to retrieve the images that users want to search. Content based Image retrieval systems attempt to search through a database to find images that are perceptually similar to a query image. Set of low-Level visual features (Color, Shape and Texture) are used to represent an image in most modern content based image retrieval systems. Therefore, between high-level information and low-level features a gap exists, which are the main reason that down the improvement of the image retrieval accuracy.In this paper Hybrid support vector machine (SVM) method proposed to retrieve several features and shorten the semantic gap between low-level visual feature and high-level perception. Image data set is taken from coral image data set. MATLAB 2009a is used as a simulator to analysis the proposed work.

Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be able to take advantage of the log data of users' feedback to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback schemes to learn effectively the correlation between low-level image features and high-level concepts. Given the error-prone nature of log data, we present a novel learning technique, named soft label support vector machine, to tackle the noisy data problem. Extensive experiments are designed and conducted to evaluate the proposed algorithms based on the COREL image data set. The promising experimental results validate the effectiveness of our log-based relevance feedback scheme empirically.

Relevance feedback has emerged as a powerful tool to boost the retrieval performance in content-based image retrieval (CBIR). In the past, most research efforts in this field have focused on designing effective algorithms for traditional relevance feedback. Given that a CBIR system can collect and store users' relevance feedback information in a history log, an image retrieval system should be able to take advantage of the log data of users' feedback to enhance its retrieval performance. In this paper, we propose a unified framework for log-based relevance feedback that integrates the log of feedback data into the traditional relevance feedback schemes to learn effectively the correlation between low-level image features and high-level concepts. Given the error-prone nature of log data, we present a novel learning technique, named soft label support vector machine, to tackle the noisy data problem. Extensive experiments are designed and conducted to evaluate the proposed algorithms based on the COREL image data set. The promising experimental results validate the effectiveness of our log-based relevance feedback scheme empirically.

In content-base image retrieval, relevance feedback (RF) schemes based on support vector machine (SVM) have been widely used to narrow the semantic gap between low-level visual features and high-level human perception. However, the performance of image retrieval with SVM active learning is known to be poor when the training data is insufficient. In this paper, the problem is solved by incorporating the unlabelled images into the learning process. We proposed a semi-supervised active learning algorithm which uses not only labeled training samples but also unlabeled ones to build better models. In relevance feedback, active learning algorithm is often used to reduce the cost of labeling by selecting only the most informative data. In addition, we introduced a semi-supervised approach which employed Nearest-Neighbor technique to label the unlabeled sample with a certain degree of uncertainty in its class information. Using these samples, Fuzzy support vector machine (FSVM) which takes into account the fuzzy nature of some training samples during its training is trained. We compared our method with standard active SVM on a database of 10,000 images, the experiment results show that the efficiency of SVM active learning can be improved by incorporating unlabelled images, and thus improve the overall retrieval performance. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2807

Region-based image retrieval and relevance feedback are two important methods to bridge the gap between the low-level visual features and the high-level semantic concepts in content-based image retrieval. In this paper, we address the issue of introducing the relevance feedback mechanism into the region-based image retrieval with online feature selection during each feedback round. Our contribution is two-fold. (1) A novel region-based image representation is proposed. Based on a generative model, a fuzzy codebook is extracted from the original region-based features, which represents the images in a uniform real-value feature space. (2) A feature selection criterion is developed and an effective relevance feedback algorithm is implemented in a boosting manner to simultaneously select the optimal features from the fuzzy codebook, and generate a strong ensemble classifier over the selected features. Experimental results show that the proposed scheme can substantially improve the retrieval performance of the region-based image retrieval system.

This paper presents a content-based image retrieval (CBIR) system that incorporates real-valued multiple instance learning (MIL) into the user relevance feedback (RF) to learn the user's subjective visual concepts, especially where the user's most interested region and how to map the local feature vector of that region to the high-level concept pattern of the user. RF provides a way to obtain the subjectivity of the user's high-level visual concepts, and MIL enables the automatic learning of the user's high-level concepts. The user interacts with the CBIR system by relevance feedback in a way that the extent to which the image samples retrieved by the system are relevant to the user's intention is labeled. The system in turn applies the MIL method to find user's most interested image region from the feedback. A multilayer neural network that is trained progressively through the feedback and learning procedure is used to map the low-level image features to the high-level concepts.

Relevance Feedback (RF) is a powerful technique in Content-Based Image Retrieval (CBIR) system and has become a very active research topic in the past few years. At the early stage of CBIR, research primarily focused on exploring various feature representation and ignored the subjectivity of human perception. There exists a gap between high-level concepts and low-level features. As an effective solution, the RF technique has been used on many CBIR systems to improve the retrieval precision. In this paper, a combined automatic weighting and relevance feedback method is proposed to improve the retrieval performance of CBIR. An approach using genetic algorithm for computing the initial weight of feature vector was introduced. By moving the query vector and updating the weighting factors simultaneously, the convergence speed of the relevance feedback retrieval is accelerated. Experimental results show that this method achieves high accuracy and effectiveness in CBIR.

In order to improve the performance of content-based image retrieval (CBIR) systems, the 'semantic gap' between the low-level visual features and the high-level semantic features attracts more and more research interest. We propose an approach to describe and to extract the fuzzy color semantics. According to human color perception model, we utilize the linguistic variable to describe the image color semantics, so it becomes possible to depict the image in linguistic expression such as mostly red. Furthermore, we apply the feedforward neural network to model the vagueness of human color perception and to extract the fuzzy semantic feature vector. Our experiments show that the color semantic features have good accordance with the human perception, and also have good retrieval performance. In some extent, our approach shows the potential to reduce the semantic gap in CBIR.

Content-Based Image Retrieval (CBIR) has become a critical technology for efficiently searching and retrieving images from large datasets based on their visual content. Traditional CBIR systems, which rely on low-level features like color, texture, and shape, often struggle with semantic gaps and scalability issues. With the rapid advancements in deep learning and cloud computing, there is a growing need to enhance CBIR performance for real-world applications. In order to tackle the issues, an enhanced CBIR process leveraging advanced neural networks, particularly Convolutional Neural Networks (CNNs), Siamese Networks, and attention mechanisms is proposed. It integrates multimodal data, including text and audio, to improve retrieval accuracy. Additionally, cloud-based infrastructure is employed to support large-scale image processing, enabling faster retrieval times and real-time performance. Edge computing techniques are also incorporated to reduce latency in applications requiring immediate responses. The proposed model demonstrates significant improvements in retrieval accuracy and efficiency compared to traditional CBIR methods. Deep learning models, particularly CNNs with transfer learning and attention mechanisms, effectively capture high-level semantic features. The integration of cloud infrastructure enhances scalability and real-time processing capabilities, while multimodal retrieval improves search relevance. The use of explainable AI techniques adds transparency to the decision-making process, increasing user trust. Hence, the advanced neural networks, coupled with cloud and edge computing, can significantly optimize CBIR systems, making them more robust, scalable, and applicable to a wide range of industries such as healthcare, security, and e-commerce.

The present paper describes a query-by-sketch image retrieval system aimed at reducing the semantic gap by adopting relevance feedback. To reduce the semantic gap between low-level visual features and high-level semantics, in this content-based image retrieval system, users' sketches play an important role in relevance feedback. When users mark similar images of output images with "relevant" labels, the "relevant" images are relevant to the sketch image in positive feedback. This method was applied to 5,500 images in Corel Photo Gallery. Experimental results show that the proposed method is effective in retrieving images.