Major challenge in automatic image annotation is bridging the semantic gap between the
computable low-level image features and the human-like interpretation of images. The …

Automatic image annotation involves automatically assigning useful keywords to an
unlabelled image. The major goal is to bridge the so-called semantic gap between the …

Image segmentation and classification are basic operations in image analysis and
multimedia search which have gained great attention over the last few years due to the large …

Color is the visual perceptual property of humans. Color obtains from the band spectrum
light-weight {of sunshine}(dissemination of wavelength versus light energy) interacting …

Automatic image annotation (AIA) is the process by which metadata, in form of keywords or
text descriptions are automatically assigned to an unlabeled image. Generally, two problems …

For a multi-level image classification, a knowledge representation scheme based on Fuzzy
Petri Net with fuzzy inference algorithms is used. A simple graphical Petri net notation and a …

Content-based image retrieval aims to acquire images from huge databases by analyzing
their visual features like color, texture, shape, and spatial relationship. The search for …

In order to exploit the massive image information and to handle overload, techniques for
analyzing image content to facilitate indexing and retrieval of images have emerged. In this …

Saliency cuts aims to segment salient objects from a given saliency map. The existing
saliency cuts methods focus on dealing with RGB images and videos, but ignore the …

Semantic analysis of multimedia content is an ongoing research area that has gained a lot of
attention over the last few years. Additionally, machine learning techniques are widely used …