Proceedings ArticleDOI
Content-Based Image Retrieval using Shape Information of Central Object
Se-Young Jang,Hoon-Sung Kwak +1 more
- Vol. 1, pp 502-505
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TLDR
The study uses the object definition of image and color correlogram in order to re-establish the image retrieval area as an objective area and to solve deteriorated performance due to color retrieval, the image shape info is used.Abstract:
Images in a database have different sizes, which may not be processed equally. In addition, if normalizing the sizes uniformly, it would inconveniently need another pre-process prior to extracting the characteristics. The study uses the object definition of image and color correlogram in order to re-establish the image retrieval area as an objective area. In addition, to solve deteriorated performance due to color retrieval, the image shape info is used. The defined area is not significantly influenced by background color, so the proposed method may have better results in the retrieval priority.read more
Citations
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Journal ArticleDOI
Gradually focused fine-grained sketch-based image retrieval.
TL;DR: A gradually focused bilinear attention model is designed to extract detailed information more effectively in fine-grained image retrieval based on sketches and outperforms the state-of-the-art sketch-based image retrieval methods.
Dissertation
Estudo comparativo de descritores para recuperação de imagens por conteudo na web
TL;DR: In this article, a dissertacao realizado nesta disserta and feito em duas abordagens is presented, where the authors compare different types of descritors of images, such as cor, textura, and forma de objects, for indexacao and recuperacao.
References
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Proceedings ArticleDOI
Image indexing using color correlograms
TL;DR: Experimental evidence suggests that this new image feature called the color correlogram outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval.
Journal ArticleDOI
Algorithms for defining visual regions-of-interest: comparison with eye fixations
C.M. Privitera,L.W. Stark +1 more
TL;DR: This paper investigates and develops a methodology that serves to automatically identify a subset of aROIs (algorithmically detected ROIs) using different image processing algorithms (IPAs), and appropriate clustering procedures, and compares hROIs with hROI as a criterion for evaluating and selecting bottom-up, context-free algorithms.
Proceedings ArticleDOI
On measuring low-level saliency in photographic images
Jiebo Luo,A. Singhal +1 more
TL;DR: This work proposes an auto-scaled, amorphous neighborhood as the context model to obtain reliable measurements of relative saliency features and shows that the proposed model is capable of generating predicates more consistent with perceived saliency.
Book ChapterDOI
Image Classification into Object / Non-object Classes
TL;DR: A method that automatically classifies the images into the object and non-object images by combining three measures based on the characteristics of an object, trained by training the neural network.
Semantics Retrieval by Content and Context of Image Regions
TL;DR: A novel approach for semantics retrieval from images in multimedia databases using color-texture classification to generate the codebook which is used to segment images into regions, which outperforms the traditional CBIR approaches.