A Comprehensive Review on Multimedia Retrieval Techniques

Abstract: With the prevalence of sight and sound advancements and web mediums, client can't fulfil with the customarey techniques for data retrieval systems. On account of this, the substance based picture recovery is turning into another and quick strategy for data recovery. Substance based picture recovery is the system for recovering the information especially pictures from a wide gathering of databases. The recovery is careried out by utilizing highlights. Content Based Image Retrieval (CBIR) is a system to compose the wide mixture of pictures by their visual highlight. Feature based recovery or retrieval procedures aree accessible for recovering the pictures, in our review we aree investigating them. In our first segment, we aree tending towareds a few nuts and bolts of a specific CBIR framework with that we have demonstrated some fundamental highlights of any picture, these aree similare to shape, surface, shading and indicated diverse systems to compute them. We have also demonstrated diverse separeation measuring systems utilized for closeness estimation of any picture furthermore talked about indexing methods. At last conclusion and future degree is examined. DOI: 10.17762/ijritcc2321-8169.15061

Practical performance of image retrieval methods

Abstract. Image retrieval is an important category of machine vision which examines the distances and similarities between images. It has many use-cases in archiving, object detection, localization and few-shot recognition. This thesis examines the problem of image retrieval in which set of images are retrieved from large-scale database based on their similarity to a query image. The problem and its different aspects are examined in this thesis as well as its history. The influence of recent development of deep learning is also covered. We experiment few different types of image retrieval problems with some recent, open-source methods and see how deep learning methods specialising in image retrieval outperform in cases where image contents are more important and classical feature extraction work better with purely visual tasks. The best results with visual tasks achieved at most two thirds accurate retrievals while with the semantic task only one in two. This implies that there is still work to do for efficient image retrieval methods.Kuvahaun menetelmien käytännön suorituskyky. Tiivistelmä. Kuvahaku on konenäön tärkeä osa-alue, joka tarkastelee kuvien välisiä etäisyyksiä ja samankaltaisuuksia. Sillä on useita käyttökohteita arkistoinnissa, objektin havaitsemisessa, paikannuksessa ja muutaman otoksen tunnistamisessa. Tämä työ käsittelee kuvahaun ongelmaa, jossa tietokannasta haetaan hakukuvalla saman näköisiä kuvia. Tätä ongelmaa ja sen eri kulmia käsitellään niinkuin myös sen historiaa. Viimeaikojen tekoälyn kehityksen vaikutus käsitellään myös. Työssä testataan paria erilaista kuvahakuongelmaa muutamalla viimeaikaisella, avoimella metodilla, ja nähdään kuinka syväoppivat, erikoistuneet metodit pärjäävät paremmin tapauksissa, joissa kuvan sisällöllä on väliä ja klassiset piirteenirroittajat paremmin visuaalisemmissa ongelmissa. Parhaimmat tulokset visuaalisissa tehtävissä saivat kaksi kolmasosaa hauista oikein ja semanttisissa tehtävissä vain puolet. Tämä viittaa siihen, että tehokkaiden kuvahakumetodien saavuttaminen vaatii vielä työtä

How automated image analysis techniques help scientists in species identification and classification?

Identification of taxonomy at a specific level is time consuming and reliant upon expert ecologists. Hence the demand for automated species identification incre­ased over the last two decades. Automation of data classification is primarily focussed on images while incorporating and analysing image data has recently become easier due to developments in computational technology. Research ef­forts on identification of species include specimens’ image processing, extraction of identical features, followed by classifying them into correct categories. In this paper, we discuss recent automated species identification systems, mainly for categorising and evaluating their methods. We reviewed and compared different methods in step by step scheme of automated identification and classification systems of species images. The selection of methods is influenced by many variables such as level of classification, number of training data and complexity of images. The aim of writing this paper is to provide researchers and scientists an extensive background study on work related to automated species identification, focusing on pattern recognition techniques in building such systems for biodiversity studies. (Folia Morphol 2018; 77, 2: 179–193

Enhancing Automatic Annotation for Optimal Image Retrieval

Image search and retrieval based on content is very cumbersome task particularly when the image database is large. The accuracy of the retrieval as well as the processing speed are two important measures used for assessing and comparing the effectiveness of various systems. Text retrieval is more mature and advanced than image content retrieval. In this dissertation, the focus is on converting image content into text tags that can be easily searched using standard search engines where the size and speed issues of the database have been already dealt with. Therefore, image tagging becomes an essential tool for image retrieval from large image databases. Automation of image tagging has received considerable attention by many researchers in recent years. The optimal goal of image description is to automatically annotate images with tags that semantically represent the image content. The speed and accuracy of Image retrieval from large databases are few of the important domains that can benefit from automatic tagging. In this work, several state of the art image classification and image tagging techniques are reviewed. We propose a new self-learning multilayered tagging framework that can address the limitations of current approaches and provide mutual accuracy improvement between the recognition layer and the annotation layer. Our results indicate that the proposed framework can improve the overall accuracy of information retrieval in a variety of image databases

Intelligent Image Retrieval Techniques: A Survey

AbstractIn the current era of digital communication, the use of digital images has increased for expressing, sharing and interpreting information. While working with digital images, quite often it is necessary to search for a specific image for a particular situation based on the visual contents of the image. This task looks easy if you are dealing with tens of images but it gets more difficult when the number of images goes from tens to hundreds and thousands, and the same content-based searching task becomes extremely complex when the number of images is in the millions. To deal with the situation, some intelligent way of content-based searching is required to fulfill the searching request with right visual contents in a reasonable amount of time. There are some really smart techniques proposed by researchers for efficient and robust content-based image retrieval. In this research, the aim is to highlight the efforts of researchers who conducted some brilliant work and to provide a proof of concept for intelligent content-based image retrieval techniques