Finding Waldo, or focus of attention using local color information

1995 ◽  
Vol 17 (8) ◽  
pp. 805-809 ◽  
Author(s):  
F. Ennesser ◽  
G. Medioni
Keyword(s):  
Focus Of Attention ◽  
Color Information ◽  
Local Color

scholarly journals Mining Regional Co-Occurrence Patterns for Image Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Zhihang Ji ◽  
Sining Wu ◽  
Fan Wang ◽  
Lijuan Xu ◽  
Yan Yang ◽  
...  
Keyword(s):  
Image Classification ◽  
Large Scale ◽  
Spatial Information ◽  
Multiple Kernel Learning ◽  
The Novel ◽  
Color Information ◽  
Local Color ◽  
Structure Information ◽  
Visual Words ◽  
Occurrence Patterns

In the context of image classification, bag-of-visual-words mode is widely used for image representation. In recent years several works have aimed at exploiting color or spatial information to improve the representation. In this paper two kinds of representation vectors, namely, Global Color Co-occurrence Vector (GCCV) and Local Color Co-occurrence Vector (LCCV), are proposed. Both of them make use of the color and co-occurrence information of the superpixels in an image. GCCV describes the global statistical distribution of the colorful superpixels with embedding the spatial information between them. By this way, it is capable of capturing the color and structure information in large scale. Unlike the GCCV, LCCV, which is embedded in the Riemannian manifold space, reflects the color information within the superpixels in detail. It records the higher-order distribution of the color between the superpixels within a neighborhood by aggregating the co-occurrence information in the second-order pooling way. In the experiment, we incorporate the two proposed representation vectors with feature vector like LLC or CNN by Multiple Kernel Learning (MKL) technology. Several challenging datasets for visual classification are tested on the novel framework, and experimental results demonstrate the effectiveness of the proposed method.

Gender Recognition of Human Faces Using Color

2008 ◽  
Vol 19 (12) ◽  
pp. 1242-1246 ◽  
Author(s):  
Adrian Nestor ◽  
Michael J. Tarr
Keyword(s):  
Color Space ◽  
Reverse Correlation ◽  
Gender Recognition ◽  
Color Information ◽  
Local Color ◽  
Face Categorization ◽  
Color Differences ◽  
The Face ◽  
Human Faces ◽  
Spatial Locations

A continuing question in the object recognition literature is whether surface properties play a role in visual representation and recognition. Here, we examined the use of color as a cue in facial gender recognition by applying a version of reverse correlation to face categorization in CIE L∗a∗b∗ color space. We found that observers exploited color information to classify ambiguous signals embedded in chromatic noise. The method also allowed us to identify the specific spatial locations and the components of color used by observers. Although the color patterns found with human observers did not accurately mirror objective natural color differences, they suggest sensitivity to the contrast between the main features and the rest of the face. Overall, the results provide evidence that observers encode and can use the local color properties of faces, in particular, in tasks in which color provides diagnostic information and the availability of other cues is reduced.

scholarly journals GRID: GRID Resample by Information Distribution

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1417
Author(s):  
Cheng Li ◽  
Baolong Guo ◽  
Zhe Huang ◽  
Jianglei Gong ◽  
Xiaodong Han ◽  
...  
Keyword(s):  
Local Concentration ◽  
Context Aware ◽  
Color Information ◽  
Expected Number ◽  
Local Color ◽  
Grid Sampling ◽  
Color Uniformity ◽  
Segmentation Algorithms ◽  
Simple Linear Iterative Clustering ◽  
Coarse To Fine

This paper exploits a concise yet efficient initialization strategy to optimize grid sampling-based superpixel segmentation algorithms. Rather than straight distributing all initial seeds evenly, it adopts a context-aware approach to modify their positions and total number via a coarse-to-fine manner. Firstly, half the expected number of seeds are regularly sampled on the image grid, thereby creating a rough distribution of color information for all rectangular cells. A series of fission is then performed on cells that contain excessive color information recursively. In each cell, the local color uniformity is balanced by a dichotomy on one original seed, which generates two new seeds and settles them to spatially symmetrical sub-regions. Therefore, the local concentration of seeds is adaptive to the complexity of regional information. In addition, by calculating the amount of color via a summed area table (SAT), the informative regions can be located at a very low time cost. As a result, superpixels are produced from ideal original seeds with an exact number and exhibit better boundary adherence. Experiments demonstrate that the proposed strategy effectively promotes the performance of simple linear iterative clustering (SLIC) and its variants in terms of several quality measures.

scholarly journals Visual contour tracking based on inner-contour model particle filter under complex background

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Songxiao Cao ◽  
Xuanyin Wang
Keyword(s):  
Particle Filter ◽  
Contour Tracking ◽  
Computationally Efficient ◽  
Color Information ◽  
Local Color ◽  
Edge Information ◽  
Normal Line ◽  
Contour Model ◽  
Complex Background ◽  
Visual Contour

AbstractIn this paper, a novel particle filter–based visual contour tracking method is proposed, which uses inner-contour model to track contour object under complex background. The purpose is to achieve effectiveness and robustness against complex background. To that end, the proposed method first utilized Sobel edge detector to detect the edge information along the normal line of the contour. Then, it sampled the inner part of the normal line to get the local color information, which was then combined with the edge information to construct new normal line likelihood. After that, all the inner color information was used to construct global color likelihood. Finally, the edge information, local color information, and global color information were fused into new observation likelihood. Experimental results showed that the proposed method was robust for contours tracking under complex background, and it was also computationally efficient and can run in real-time completely.

Sign in / Sign up

Export Citation Format

Share Document