Steganography is the method of information hiding. Free selection of cover image is a particular preponderance of steganography to other information hiding techniques. The performance of steganographic system can be improved by selecting the reasonable cover image. This article presents two level unsupervised image classification algorithm based on statistical characteristics of the image which...

Part-based representations have been shown to be very useful for image classification. Learning part-based models is often viewed as a two-stage problem. First, a collection of informative parts is discovered, using heuristics that promote part distinctiveness and diversity, and then classifiers are trained on the vector of part responses. In this paper we unify the two stages and learn the ima...

We show how high-level scene properties can be inferred from classiication of low-level image features, speciically for the indoor-outdoor scene retrieval problem. We systematically studied the features: (1) his-tograms in the Ohta color space (2) multiresolution, simultaneous autoregressive model parameters (3) coef-cients of a shift-invariant DCT. We demonstrate that performance is improved b...

Image classification using low-level features is always a challenging research in computer vision. Recent years, content-based image retrieval has emerged as an important area in computer vision and multimedia computing. In this project, I'm going to introduce and implement an approach [1] that can better represent the image using low-level features and then I apply this method in image classif...

Advance image classification system focuses on synthetic (e.g non-photographic) & Natural (e.g photographic) images. The classification of images based on semantic description is a challenging and important problem in automatic image identification. An algorithm for natural and synthetic image classification has been developed. Some features are extracted from the raw images data in order to ex...

A framework for deriving high-level scene attributes from low-level image features is presented. The assignment of the attributes to images is done by a hierarchical classification of the low level features, which capture colour, texture and spatial information. A system for image classification is implemented, which aids in the evaluation of the different methods available. A detailed analysis...

By taking into account the properties and limitations of the human visual system, images can be more efficiently compressed, colors more accurately reproduced, prints better rendered. To show all these advantages in this paper new adapted color charts have been created based on technical and visual image category analysis. A number of tests have been carried out using extreme images with their ...

Article history: Received 12 October 2014 Received in revised form 26 December 2014 Accepted 1 January 2015 Available online 25 February 2015