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JournalISSN: 2192-662X

International Journal of Multimedia Information Retrieval 

Springer Science+Business Media
About: International Journal of Multimedia Information Retrieval is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Computer science & Artificial intelligence. It has an ISSN identifier of 2192-662X. Over the lifetime, 261 publications have been published receiving 4165 citations. The journal is also known as: IJMIR & Multimedia information retrieval.


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Journal ArticleDOI
TL;DR: The field of semantic segmentation as pertaining to deep convolutional neural networks is reviewed and comprehensive coverage of the top approaches is provided and the strengths, weaknesses and major challenges are summarized.
Abstract: During the long history of computer vision, one of the grand challenges has been semantic segmentation which is the ability to segment an unknown image into different parts and objects (e.g., beach, ocean, sun, dog, swimmer). Furthermore, segmentation is even deeper than object recognition because recognition is not necessary for segmentation. Specifically, humans can perform image segmentation without even knowing what the objects are (for example, in satellite imagery or medical X-ray scans, there may be several objects which are unknown, but they can still be segmented within the image typically for further investigation). Performing segmentation without knowing the exact identity of all objects in the scene is an important part of our visual understanding process which can give us a powerful model to understand the world and also be used to improve or augment existing computer vision techniques. Herein this work, we review the field of semantic segmentation as pertaining to deep convolutional neural networks. We provide comprehensive coverage of the top approaches and summarize the strengths, weaknesses and major challenges.

451 citations

Journal ArticleDOI
TL;DR: An overview of the literature concerning the automatic analysis of images of printed and handwritten musical scores and a reference scheme for any researcher wanting to compare new OMR algorithms against well-known ones is presented.
Abstract: For centuries, music has been shared and remembered by two traditions: aural transmission and in the form of written documents normally called musical scores. Many of these scores exist in the form of unpublished manuscripts and hence they are in danger of being lost through the normal ravages of time. To preserve the music some form of typesetting or, ideally, a computer system that can automatically decode the symbolic images and create new scores is required. Programs analogous to optical character recognition systems called optical music recognition (OMR) systems have been under intensive development for many years. However, the results to date are far from ideal. Each of the proposed methods emphasizes different properties and therefore makes it difficult to effectively evaluate its competitive advantages. This article provides an overview of the literature concerning the automatic analysis of images of printed and handwritten musical scores. For self-containment and for the benefit of the reader, an introduction to OMR processing systems precedes the literature overview. The following study presents a reference scheme for any researcher wanting to compare new OMR algorithms against well-known ones.

246 citations

Journal ArticleDOI
TL;DR: In this paper, a survey of instance segmentation, its background, issues, techniques, evolution, popular datasets, related work up to the state of the art and future scope have been discussed.
Abstract: Object detection or localization is an incremental step in progression from coarse to fine digital image inference. It not only provides the classes of the image objects, but also provides the location of the image objects which have been classified. The location is given in the form of bounding boxes or centroids. Semantic segmentation gives fine inference by predicting labels for every pixel in the input image. Each pixel is labelled according to the object class within which it is enclosed. Furthering this evolution, instance segmentation gives different labels for separate instances of objects belonging to the same class. Hence, instance segmentation may be defined as the technique of simultaneously solving the problem of object detection as well as that of semantic segmentation. In this survey paper on instance segmentation, its background, issues, techniques, evolution, popular datasets, related work up to the state of the art and future scope have been discussed. The paper provides valuable information for those who want to do research in the field of instance segmentation.

195 citations

Journal ArticleDOI
TL;DR: A new algorithm using directional local extrema patterns meant for content-based image retrieval application that shows a significant improvement in terms of their evaluation measures as compared with other existing methods on respective databases.
Abstract: In this paper, a new algorithm using directional local extrema patterns meant for content-based image retrieval application is proposed. The standard local binary pattern (LBP) encodes the relationship between reference pixel and its surrounding neighbors by comparing gray-level values. The proposed method differs from the existing LBP in a manner that it extracts the directional edge information based on local extrema in 0 $$^{\circ }$$ , 45 $$^{\circ }$$ , 90 $$^{\circ }$$ , and 135 $$^{\circ }$$ directions in an image. Performance is compared with LBP, block-based LBP (BLK_LBP), center-symmetric local binary pattern (CS-LBP), local edge patterns for segmentation (LEPSEG), local edge patterns for image retrieval (LEPINV), and other existing transform domain methods by conducting four experiments on benchmark databases viz. Corel (DB1) and Brodatz (DB2) databases. The results after being investigated show a significant improvement in terms of their evaluation measures as compared with other existing methods on respective databases.

171 citations

Journal ArticleDOI
TL;DR: In this article, the authors identify and shed light on what they believe are the most pressing challenges in recommender systems from both academic and industry perspectives, and detail possible future directions and visions for the further evolution of the field.
Abstract: Music recommender systems (MRSs) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user’s fingertip. While today’s MRSs considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user–item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art toward solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field.

146 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202315
202249
202120
202019
201919
201821