A Multimodal Constellation Model for Object Image Classification

A Multimodal Constellation Model for Object Category Recognition

Efficient methods for object recognition using the constellation model

We present efficient methods for object recognition, using the constellation model. This model represents objects as constellations of rigid features, with the variability between them being represented by a joint probability density function. Using simple, exhaustive methods, the computational requirements for this approach quickly become prohibitive. By using A* search methods, accompanied by...

Sample-oriented Domain Adaptation for Image Classification

Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. The conventional image processing algorithms cannot perform well in scenarios where the training images (source domain) that are used to learn the model have a different distribution with test images (target domain). Also, many real world applicat...

application of image fusion (object fusion) for forest classification in northern forests of iran

forest classification on the basis of satellite images is a promising technique both for primary map production and for map updating and forest monitoring. for accurate for-est classification into three classes, using mapping by canopy cover density “high spatial resolution satellite images have to be used in order to obtain the required spatial detail” [schneider, 1999]. at the same time, the ...

Image Classification into Object / Non-object Classes

We propose a method that automatically classifies the images into the object and non-object images. An object image is an image with object(s). An object in an image is defined as a set of regions located near the center of the image, which has significant color distribution compared with its surrounding (or background) region. We define three measures for the classification based on the charac...