A Novel Filtering and Coding Method for Image Classification and Image Retrieval

2015 ◽  
Author(s):  
Rupali Patil ◽  
Amrita Manjrekar
Keyword(s):  
Image Retrieval ◽  
Image Classification ◽  
Coding Method

A new discriminative coding method for image classification

2014 ◽  
Vol 21 (2) ◽  
pp. 133-145 ◽  
Author(s):  
Xiaoshan Yang ◽  
Tianzhu Zhang ◽  
Changsheng Xu
Keyword(s):  
Image Classification ◽  
Coding Method

scholarly journals Picture semantic similarity search based on bipartite network of picture-tag type

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259028
Author(s):  
Mingxi Zhang ◽  
Liuqian Yang ◽  
Yipeng Dong ◽  
Jinhua Wang ◽  
Qinghan Zhang
Keyword(s):  
Image Retrieval ◽  
Image Classification ◽  
Semantic Similarity ◽  
Similarity Search ◽  
Recommendation System ◽  
Search Method ◽  
Visual Features ◽  
Bipartite Network ◽  
Effectiveness And Efficiency ◽  
Image Recommendation

Searching similar pictures for a given picture is an important task in numerous applications, including image recommendation system, image classification and image retrieval. Previous studies mainly focused on the similarities of content, which measures similarities based on visual features, such as color and shape, and few of them pay enough attention to semantics. In this paper, we propose a link-based semantic similarity search method, namely PictureSim, for effectively searching similar pictures by building a picture-tag network. The picture-tag network is built by “description” relationships between pictures and tags, in which tags and pictures are treated as nodes, and relationships between pictures and tags are regarded as edges. Then we design a TF-IDF-based model to removes the noisy links, so the traverses of these links can be reduced. We observe that “similar pictures contain similar tags, and similar tags describe similar pictures”, which is consistent with the intuition of the SimRank. Consequently, we utilize the SimRank algorithm to compute the similarity scores between pictures. Compared with content-based methods, PictureSim could effectively search similar pictures semantically. Extensive experiments on real datasets to demonstrate the effectiveness and efficiency of the PictureSim.

Content-Based Image Classification and Retrieval

2011 ◽  
pp. 68-83 ◽  
Author(s):  
Jafar M. Ali
Keyword(s):  
Image Retrieval ◽  
Image Classification ◽  
Data Storage ◽  
Rough Set ◽  
Texture Features ◽  
Feature Reduction ◽  
Image Texture ◽  
Content Based Image Retrieval ◽  
Attribute Vector ◽  
Real Value

Advances in data storage and image acquisition technologies have enabled the creation of large image datasets. Thus, it is necessary to develop appropriate information systems to efficiently manage these datasets. Image classification and retrieval is one of the most important services that must be supported by such systems. The most common approach used is content-based image retrieval (CBIR) systems. This paper presents a new application of rough sets to feature reduction, classification, and retrieval for image databases in the framework of content-based image retrieval systems. The suggested approach combines image texture features with color features to form a powerful discriminating feature vector for each image. Texture features are extracted, represented, and normalized in an attribute vector, followed by a generation of rough set dependency rules from the real value attribute vector. The rough set reduction technique is applied to find all reducts with the minimal subset of attributes associated with a class label for classification.

scholarly journals Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Afshan Latif ◽  
Aqsa Rasheed ◽  
Umer Sajid ◽  
Jameel Ahmed ◽  
Nouman Ali ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Image Classification ◽  
Image Representation ◽  
Feature Representation ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Comprehensive Review ◽  
Visual Understanding

Multimedia content analysis is applied in different real-world computer vision applications, and digital images constitute a major part of multimedia data. In last few years, the complexity of multimedia contents, especially the images, has grown exponentially, and on daily basis, more than millions of images are uploaded at different archives such as Twitter, Facebook, and Instagram. To search for a relevant image from an archive is a challenging research problem for computer vision research community. Most of the search engines retrieve images on the basis of traditional text-based approaches that rely on captions and metadata. In the last two decades, extensive research is reported for content-based image retrieval (CBIR), image classification, and analysis. In CBIR and image classification-based models, high-level image visuals are represented in the form of feature vectors that consists of numerical values. The research shows that there is a significant gap between image feature representation and human visual understanding. Due to this reason, the research presented in this area is focused to reduce the semantic gap between the image feature representation and human visual understanding. In this paper, we aim to present a comprehensive review of the recent development in the area of CBIR and image representation. We analyzed the main aspects of various image retrieval and image representation models from low-level feature extraction to recent semantic deep-learning approaches. The important concepts and major research studies based on CBIR and image representation are discussed in detail, and future research directions are concluded to inspire further research in this area.

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