Coding of multiple proteins by overlapping reading frames is not a feature one would associate with eukaryotic genes. Indeed, codependency between codons of overlapping protein-coding regions imposes a unique set of evolutionary constraints, making it a costly arrangement. Yet in cases of tightly coexpressed interacting proteins, dual coding may be advantageous. Here we show that although dual ...

Visual pattern processing becomes increasingly complex along the ventral pathway, from the low-level coding of local orientation in the primary visual cortex to the high-level coding of face identity in temporal visual areas. Previous research using pattern aftereffects as a psychophysical tool to measure activation of adaptive feature coding has suggested that awareness is relatively unimporta...

Intraprediction is one of the most complex parts High-Efficiency Video Coding (HEVC), because it selects best prediction mode by calculating cost every Unit (CU), which provides higher complexity intracoding. Visual saliency map can show attention regions human eyes, generated certain static and space-time detection method. By analyzing percentage coding time for different size CU, relation vis...

The video coding standard MPEG-4 is enabling content-based functionalities of a prior decomposition of sequences into video object planes (VOP) so that each VOP represents a semantic object. Therefore extraction of semantic objects is an important part. There are various coding tools: shape coding, motion estimation and compensation, texture coding, multifunctional coding, error resilience, spr...

Effective and efficient texture feature extraction and classification is an important problem in image understanding and recognition. Recently, texton learning based texture classification approaches have been widely studied, where the textons are usually learned via K -means clustering or sparse coding methods. However, the K -means clustering is too coarse to characterize the complex feature ...

In this paper, we present the learning strategies and feature extraction techniques that were applied by the IBM Research Australia team to the Medical Clustering challenge of ImageCLEF 2015. The challenge is to automatically annotate and categorize X-ray images into head-neck, body, upper-limb, lower-limb and foreign object categories. Our proposed methodology and details of experiments for ea...

Topic identification as a specific case of text classification is one of the primary steps toward knowledge extraction from the raw textual data. In such tasks, words are dealt with as a set of features. Due to high dimensionality and sparseness of feature vector result from traditional feature selection methods, most of the proposed text classification methods for this purpose lack performance...

The problem of compressing a large collection of feature vectors is investigated, so that object identification can be processed on the compressed form of the features. The idea is to perform matching of a query image against an image database, using directly the compressed form of the descriptor vectors, without decompression. Specifically, we concentrate on the Scale Invariant Feature Transfo...

We propose an image classification framework by leveraging the non-negative sparse coding, correlation constrained low rank and sparse matrix decomposition technique (CCLR-ScSPM). First, we propose a new non-negative sparse coding along with max pooling and spatial pyramid matching method (ScSPM) to extract local feature’s information in order to represent images, where non-negative sparse codi...

A main distinguishing feature of a wireless network compared with a wired network is its broadcast nature, in which the signal transmitted by a node may reach several other nodes, and a node may receive signals from several other nodes, simultaneously. Rather than a blessing, this feature is treated more as an interference-inducing nuisance in most wireless networks today (e.g., IEEE 802.11). T...