Results 331 to 340 of about 678,145 (379)

Attentive Content-Based Image Retrieval

2016
In this chapter, we show how visual attention can enhance the performance in object recognition. This proposition is inspired by the idea introduced in Neisser (Cognitive psychology. Appleton-Century-Crofts, New York, 1967), which indicates that object recognition in human perception consists of two steps: an “attentional process selects the region of ...
Awad, Dounia, Courboulay, Vincent, Revel, Arnaud   +2 more
openaire   +3 more sources

Content based image and video retrieval [PDF]

SPIE Proceedings, 2010
The growing capacity of computers, the abundance of digital cameras and the increased connectivity of the world all point to large digital multimedia archives. They include images and videos from the World Wide Web, museum objects, flowers, trademarks, and views from everyday life.
B. S. Patil, T H Mohite, Shubhangi H. Patil, P. P. Belegali, D Dhobale Dhanashri   +4 more
openaire   +1 more source

Content-Based Image Retrieval

2005
Sensing and processing multimedia information is one of the basic traits of human beings: The audiovisual system registers and transports surrounding images and sounds. This complex re­cording system, complemented by the senses of touch, taste, and smell, enables perception and provides humans with data for analysing and interpreting the environment ...
Timo R. Bretschneider, Odej Kao
openaire   +1 more source

Content Based Image Retrieval for Unsegmented Images

2003
We present a new method for image retrieval by shape similarity able to deal with real images with not uniform background and possible touching/occluding objects. First of all we perform a sketch-driven segmentation of the scene by means of a Deformation Tolerant version of the Generalized Hough Transform (DTGHT). Using the DTGHT we select in the image
M. ANELLI, A. MICARELLI, SANGINETO E
openaire   +4 more sources

Saliency Inside: Learning Attentive CNNs for Content-Based Image Retrieval

IEEE Transactions on Image Processing, 2019
In content-based image retrieval (CBIR), one of the most challenging and ambiguous tasks is to correctly understand the human query intention and measure its semantic relevance with images in the database.
Shikui Wei, Lixin Liao, Jia Li, Qinjie Zheng, Fei Yang, Yao Zhao   +5 more
semanticscholar   +1 more source

Content-based retrieval for remotely sensed images

SPIE Proceedings, 2001
The work describes an innovative technique to automatically extract and manage remote sensing image-content. Simple but very flexible numeric recognition methodologies allow the content-based retrieval from huge remotely sensed image database. The most important result of this methodology is a tool for the information retrieval based on example.
Michele Bruzzo, Ferdinando Giordano, Laura Pagani, DELLEPIANE, SILVANA, Giancarlo Bo   +4 more
openaire   +4 more sources

Feature Extraction for Content-Based Image Retrieval

2009
Feature extraction for content-based image retrieval is the process of automatically computing a compact representation of some attribute of digital images, to be used to derive information about image contents. Feature extraction is a fundamental task for any application requiring the analysis of images, as cultural heritage services, digital catalogs,
SCHETTINI, RAIMONDO, Gagliardi, I, CIOCCA, GIANLUIGI   +2 more
openaire   +4 more sources

Content-based image retrieval by multimodal interaction

IECON'03. 29th Annual Conference of the IEEE Industrial Electronics Society (IEEE Cat. No.03CH37468), 2004
Due to the size of today's professional image databases, the standard approach to content-based image retrieval is to interactively navigate through the content. However, most people whose job necessitates working with such databases do not have a technical background.
Bauckhage, Christian, Käster, Thomas, Pfeiffer, Michael, Sagerer, Gerhard   +3 more
openaire   +3 more sources

On the Usage of Clustering for Content Based Image Retrieval

2007
Retrieval of images based on the content is a process that requires the comparison of the multidimensional representation of the contents of a given example with all of those images in the database. To speed up this process, several indexing techniques have been proposed. All of them do efficiently the work up to 30 dimensions [8].
Manjarrez Sanchez, Jorge, Martinez, José, Valduriez, Patrick   +2 more
openaire   +5 more sources

Content-based image retrieval for medical infrared images

The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2005
Past efforts on the automated processing on medical infrared images has typically focused on specialized applications like the detection of breast cancer. We propose the application of content-based image retrieval (CBIR) to medical thermal images. CBIR allows the retrieval of similar images based on features directly extracted from the image data ...
Gerald Schaefer, Shao Ying Zhu, Bryan F. Jones   +2 more
openaire   +3 more sources