Content based image search over the world wide web

2019

With the abundance of multimedia content on the World Wide Web, research and learning of effective feature representation and similarity measures have become crucial. Image searching poses several challenges. Lately, many researchers have been exploring the field. Automatic annotation of images based on digital content processing proves to be an encouraging direction in the field. Content based image retrieval system development is an emerging field. Accuracy of the results of semantic search depends on the understanding of searcher’s purpose, the meaning of conditions imposed in the search query and their mapping in the searchable data space. A visual content semantic search engine is proposed in this paper. The search engine employs digital image features for searching the image database. The presented algorithm produces promising results. The performance of our algorithm is tested on an extensive set of tags and queries resulting in accurate and efficient results.

The proliferation of multimedia on the World Wide Web has led to the introduction of Web search engines for images, video, and audio. On the Web, multimedia is typically embedded within documents that provide a wealth of indexing information. Harsh computational constraints imposed by the economics of advertising-supported searches restrict the complexity of analysis that can be performed at query time. And users may be unwilling to do much more than type a keyword or two to input a query. Therefore, the primary sources of information for indexing multimedia documents are text cues extracted from HTML pages and multimedia document headers. Off-line analysis of the content of multimedia documents can be successfully employed in Web search engines when combined with these other information sources. Content analysis can be used to categorize and summarize multimedia, in addition to providing cues for finding similar documents. This paper was delivered as a keynote address at the Challenge of Image Retrieval '99. It represents a personal and purposefully selective review of image and video searching on the World Wide Web.

This work describes a .Net implementation of a content-based image search component, used for deployment and assessment of an image annotation and retrieval tool. It has been designed in a flexible and portable way, under the technology perspective, where different descriptors can be easily configured for different contexts.

2012

As the volumes of web images have grown rapidly in the last decade, Content-Based Image Retrieval (CBIR) has attracted substantial interests as an effective tool to manage the images. Most existing CBIR systems focus on the object in the image, while ignoring the conditions (day/night, sunny/rain, etc.) and the backgrounds, both of which are very helpful to meet the user's information need. To overcome this shortcoming, in this paper, we present a novel CBIR system depending on a novel query formulation considering three aspects: Object, Background and Condition. Specifically, we design a user-friendly interface to help the user formulate a query. The interface can allow the user to give the percentage, relative position and size of each object in the background. Moreover, a corresponding effective ranking method is proposed to return the desirable search results. Experimental results demonstrate that our proposed system improves the searching performance and the user experience compared with the existing searching systems.

… Challenge of Image Retrieval, University of …, 1998

While pages on the Web contain more and more multimedia information, such as images, videos and audio, today search engines are mostly based on textual information. There is an emerging need of a new generation of search engines that try to exploit the full ...

The existing image search system often faces difficulty in finding an appropriate retrieved image corresponding to an image query. The difficulty is commonly caused by the users' intention for searching image is different with dominant information of the image collected from feature extraction. In this paper, we present a new approach for the content-dependent image search system. The system utilizes information of color distribution inside an image and detects a cloud of clustered colors as something-supposed as an object. We apply segmentation of an image as a content-dependent process before feature extraction in order to identify is there any object or not inside an image. The system extracts 3 features, which are color, shape, and texture features and aggregates these features for similarity measurement between an image query and image database. HSV histogram color is used to extract the color feature of the image. While the shape feature extraction used Connected Component Labeling (CCL) which is calculated the area value, equivalent diameter, extent, convex hull, solidity, eccentricity, and perimeter of each object. The texture feature extraction used Leung Malik (LM)'s approach with 15 kernels. For applicability of our proposed system, we applied the system with benchmark 1000 image SIMPLIcity dataset consisting of 10 categories namely Africans, beaches, buildings historians, buses, dinosaurs, elephants, roses, horses, mountains, and food. The experimental results performed 62% accuracy rate to detect objects by color feature, 71% by texture feature, 60% by shape feature, 72% by combined color-texture feature, 67% by combined color-shape feature, 72 % combined texture-shape features and 73% combined all features.

CBIR is the study of browsing digital images from large database collection. This is a growing research area having many applications in the fields of image processing, pattern recognition, medical fields etc. In most image retrieval systems image is represented as a set of low level features. In this image retrieval paper comparative study is performed on color, texture and color-texture using various internal semantic properties using different methods. In this work texture features are extracted using Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction. The images are retrieved according to user satisfaction and thereby reduce the semantic gap between low level features and high level features. Again Color features are extracted using RGB, DCT and HSV methods and compared. Finally In this paper texture features and color features are combined to check the precision and recall rates for all methods. I. INTRODUCTION The term "content-based image retrieval" seems to have originated in 1992 when it was used by T. Kato to describe experiments into automatic retrieval of images from a database, based on the colors and shapes present. Since then, the term has been used to describe the process of retrieving desired images from a large collection on the basis of syntactical image features. The techniques, tools, and algorithms that are used originate from fields such as statistics, pattern recognition, signal processing, and computer vision [1]. Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases. Retrieving images from the large database is a difficult task. In the image retrieval system it analyses the contents of the image from the database image and extracts each of the feature [2]. Image retrieval can be divided into different types based on the interpretation from the user for example if user gives the text as input then it is called as " text based image retrieval " and if the user gives the image as input then it is called " image based image retrieval ". The text-based technique first annotates the images with text, and uses text-based database management systems to perform image retrieval. The content based retrieval images are indexed by their own visual content, such as color, texture or shape. Image retrieval has become more and more important with the advance of computer technology [3]. Image retrieval systems were introduced to address the problems associated with text-based image retrieval. Content based image retrieval is a set of techniques for retrieving semantically-relevant images from an image database based on automatically-derived image features. The main goal of image retrieval is efficiency during image indexing and retrieval, thereby reducing the need for human intervention in the indexing process. The computer must be able to retrieve images from a database without any human assumption on specific domain [4]. As the images grow complex and diverse, retrieving the right images becomes a difficult challenge. Image retrieval system is used to find out similar image to query image. There are different methods to search image from large database[5].-Based on Text (Query by Text): Here user gives a keyword or textual description for searching an image.-Based on Draw (Query by Sketch): Here user provides drawing or sketch of an image.-Based on Example image (Query by Example): Here user gives similar image to the query image.

IJCSMC, 2018

Image content on the Web is increasing exponentially. As a result, there is a need for image retrieval systems. Historically, there have been two methodologies, text-based and content-based. In the text-based approach, query systems retrieve images that have been manually annotated using key words. This approach can be problematic: it is labor-intensive and maybe biased according to the subjectivity of the observer. Content based image retrieval (CBIR) searches and retrieves digital images in large databases by analysis of derived-image features. CBIR systems typically use the characteristics of color, texture, shape and their combination for definition of features. Similarity measures that originated in the preceding text-based era are commonly used. However, CBIR struggles with bridging the semantic gap, defined as the division between high-level complexity of CBIR and human perception and the low-level implementation features and techniques. In this paper, CBIR is reviewed in a broad context. Newer approaches is feature generation and similarity measures are detailed with representative studies addressing their efficacy. Color-texture moments, columns-of-interest, harmonysymmetry-geometry, SIFT (Scale Invariant Feature Transform), and SURF (Speeded Up Robust Features) are presented as alternative feature generation modalities. Graph matching, Earth Mover’s Distance, and relevance feedback are discussed with the realm of similarity. We conclude that while CBIR is evolving and continues to slowly close the semantic gap, addressing the complexity of human perception remains a challenge.

2010

The difficulties faced in an image retrieval system used for browsing, searching and retrieving of image in an image databases cannot be underestimated also the efficient management of the rapidly expanding visual information has become an urgent problem in science and technology. This requirement formed the driving force behind the emergence of image retrieval techniques. Image retrieval based on content also called content based image retrieval, is a technique which uses the visual contents to search an image in the scale database. This Image retrieval technique integrate both low-level visual features, addressing the more detailed perceptual aspects, and high-level semantic features underlying the more general conceptual aspects of visual data. In connection with this Content Based Image Retrieval is a technology that is being developed to address different application areas, remote sensing, geographic information systems, and weather forecasting, architectural and engineering de...

An image retrieval and re-ranking system utilizing a visual re-ranking framework which is proposed in this paper the system retrieves a dataset from the World Wide Web based on textual query submitted by the user. These results are kept as data set for information retrieval. This dataset is then re-ranked using a visual query (multiple images selected by user from the dataset) which conveys user's intention semantically. Visual descriptors (MPEG-7) which describe image with respect to low-level feature like color, texture, etc are used for calculating distances. These distances are a measure of similarity between query images and members of the dataset. Our proposed system has been assessed on different types of queries such as apples, Console, Paris, etc. It shows significant improvement on initial text-based search results.This system is well suitable for online shopping application.