Semantic Gap In Image Retrieval Research Articles

A Semantic Image Retrieval Method Based on Interest Selection

There is a semantic gap between people’s understanding of images and the underlying visual features of images, which makes it difficult for image retrieval results to meet the needs of individual interests. To overcome the semantic gap in image retrieval, this paper proposes a semantic image retrieval method based on interest selection. This method analyses the interest points of individual selections and gives the weight of the interest selection in different regions of an image. By extracting the underlying visual features of different regions, this paper constructs two feature vector methods after users’ interest point weighting. The two methods are called interest weighted summation and interest weighting. Finally, this paper compares the accuracy of different image classification methods using a support vector machine classification algorithm. The experimental results show that the target classification accuracy of the classification algorithm based on interest weighted summation is higher than that of the traditional and interest weighted methods. The classification algorithm based on interest weighted summation has the highest overall effect on target object classification in the four experimental scenarios. Therefore, the interest point selection method can effectively improve the overall satisfaction of image recommendation and can be used as a novel solution to overcome the semantic gap.

Image retrieval via learning content-based deep quality model towards big data

Image retrieval aims to search specific image from large-scale datasets. Traditional text-based and content-based image retrieval approaches have shown competitive performance. However, both of which are limited by semantic gap, i.e., they cannot reflect human perception of images. To narrow semantic gap in image retrieval, this paper proposes a deep neural network (DNN) based image retrieval method, where saliency map is derived to form human gaze shifting paths by constraint metrics. More specifically, we first design a DNN-based image saliency prediction. Subsequently, we leverage image quality assessment (IQA) algorithm to select high-quality salient regions, which will be concatenated in sequence by using proposed constraint metrics to mimic human visual perception. Afterwards, we leverage the CNN-based architecture for deep representation acquisition of each images, where spatial structure among salient regions can be well preserved. Subsequently, based on the quality score of the query image, a series of candidate images whose quality scores are similar to that of the query image are derived. Finally, we engineer a ranking distance metric to refine the candidate images to achieve image retrieval. Extend experiments demonstrate that our method outperforms several state-of-the-art algorithms.

A new strategy for bridging the semantic gap in image retrieval

Content-based image retrieval CBIR research is currently faced with the so called the 'semantic gap' problem. CBIR researchers work at the near end of the gap, applying computer science methods to bridge the gap. Cognitive psychology researchers work at the far end of the gap, studying how humans perceive things in their environment. This article looks at the literature from a new perspective, and presents a structured analysis of what CBIR researchers have done, in their efforts to bridge the gap. This analysis suggests that more emphasis should be placed on studying the far end of the gap. The second part of this article discusses a study conducted at the far end of the gap, to begin the process of developing and testing techniques for externalising and analysing the visual concepts that are evoked by images. This more disciplined approach has the potential to guide researchers in their efforts to bridge the gap.

A new strategy for bridging the semantic gap in image retrieval

Content-based image retrieval (CBIR) research is currently faced with the so called the 'semantic gap' problem. CBIR researchers work at the near end of the gap, applying computer science methods to bridge the gap. Cognitive psychology researchers work at the far end of the gap, studying how humans perceive things in their environment. This article looks at the literature from a new perspective, and presents a structured analysis of what CBIR researchers have done, in their efforts to bridge the gap. This analysis suggests that more emphasis should be placed on studying the far end of the gap. The second part of this article discusses a study conducted at the far end of the gap, to begin the process of developing and testing techniques for externalising and analysing the visual concepts that are evoked by images. This more disciplined approach has the potential to guide researchers in their efforts to bridge the gap.

AN OBJECT PROPERTIES FILTER FOR MULTI-MODALITY ONTOLOGY SEMANTIC IMAGE RETRIEVAL

Ontology is a semantic technology that provides the possible approach to bridge the issue on semantic gap in image retrieval between low-level visual features and high-level human semantic. The semantic gap occurs when there is a discrepancy between the information that is extracted from visual data and the text description. In other words, there is a difference between the computational representation in machine and human natural language. In this paper, an ontology has been utilized to reduce the semantic gap by developing a multi-modality ontology image retrieval with the enhancement of a retrieval mechanism by using the object properties filter. To achieve this, a multi-modality ontology semantic image framework was proposed, comprising of four main components which were resource identification, information extraction, knowledge-based construction and retrieval mechanism. A new approach, namely object properties filter is proposed by customizing the semantic image retrieval algorithm and the graphical user interface to facilitate the user to engage with the machine i.e. computers, in order to enhance the retrieval performance. The experiment results showed that the proposed approach delivered better results compared to the approach that did not use the object properties filter based on probability precision measurement.

Distributed Retrieval of Images using Particle Swarm Optimization and Hadoop

based Image Retrieval (CBIR) is the problem of searching for digital images in large databases. It is the vital application of computer vision techniques to the image retrieval problem. One inherent problem associated with Content based Image Retrieval is the response time of the system to retrieve relevant result from the image database. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers. The parallel processing of Hadoop can be leveraged to efficiently retrieve images with very less response time. The proposed approach also avoids the semantic gap in image retrieval by utilizing automatic relevance feedback and meta-heuristic optimization. Automatic relevance feedback is implemented using Latent Semantic Analysis, and Particle swarm optimization provides meta-heuristic based development. The goal of proposed approach is to - cluster relevant images using meta-heuristics in less amount of time effectively.

Meta-Heuristics Based Arf Optimization for Image Retrieval

The proposed approach avoids the semantic gap in image retrieval by combining automatic relevance feedback and a modified stochastic algorithm. A visual feature database is constructed from the image database, using combined feature vector. Very few fast-computable features are included in this step. The user selects the query image, and based on that, the system ranks the whole dataset. The nearest images are retrieved and the first automatic relevance feedback is generated. The combined similarity of textual and visual feature space using Latent Semantic Indexing is evaluated and the images are labelled as relevant or irrelevant. The feedback drives a feature re-weighting process and is routed to the particle swarm optimizer. Instead of classical swarm update approach, the swarm is split, for each swarm to perform the search in parallel, thereby increasing the performance of the system. It provides a powerful optimization tool and an effective space exploration mechanism. The proposed approach aims to achieve the following goals without any human interaction - to cluster relevant images using meta-heuristics and to dynamically modify the feature space by feeding automatic relevance feedback.

Facing the reality of semantic image retrieval

PurposeTo provide a better‐informed view of the extent of the semantic gap in image retrieval, and the limited potential for bridging it offered by current semantic image retrieval techniques.Design/methodology/approachWithin an ongoing project, a broad spectrum of operational image retrieval activity has been surveyed, and, from a number of collaborating institutions, a test collection assembled which comprises user requests, the images selected in response to those requests, and their associated metadata. This has provided the evidence base upon which to make informed observations on the efficacy of cutting‐edge automatic annotation techniques which seek to integrate the text‐based and content‐based image retrieval paradigms.FindingsEvidence from the real‐world practice of image retrieval highlights the existence of a generic‐specific continuum of object identification, and the incidence of temporal, spatial, significance and abstract concept facets, manifest in textual indexing and real‐query scenarios but often having no directly visible presence in an image. These factors combine to limit the functionality of current semantic image retrieval techniques, which interpret only visible features at the generic extremity of the generic‐specific continuum.Research limitations/implicationsThe project is concerned with the traditional image retrieval environment in which retrieval transactions are conducted on still images which form part of managed collections. The possibilities offered by ontological support for adding functionality to automatic annotation techniques are considered.Originality/valueThe paper offers fresh insights into the challenge of migrating content‐based image retrieval from the laboratory to the operational environment, informed by newly‐assembled, comprehensive, live data.