Salient Region Detection via Feature Combination and Discriminative Classifier
نویسندگان
چکیده
منابع مشابه
Evaluating Local Feature Detectors in Salient Region Detection
In this work, we study local feature extraction methods and evaluate their performance in detecting local features from the salient regions of images. In order to measure the detectors’ performance, we compared the detected regions to gaze fixations obtained from the eye movement recordings of human participants viewing two types of images: natural images (photographs) and abstract/surreal imag...
متن کاملSalient Object Detection via Structure Extraction and Region Contrast
In this paper, we propose a novel salient object detection approach, which aims in suppressing distractions caused by the small scale pattern in the background and foreground. First, we employ a structure extraction algorithm as a pre-processing step to smooth the textures, eliminate high frequency components and retain the image’s main structure information. Second, we segment the texture-supp...
متن کاملSalient Region Detection and Segmentation
Detection of salient image regions is useful for applications like image segmentation, adaptive compression, and region-based image retrieval. In this paper we present a novel method to determine salient regions in images using low-level features of luminance and color. The method is fast, easy to implement and generates high quality saliency maps of the same size and resolution as the input im...
متن کاملPolynomial Network Classifier with Discriminative Feature Extraction
The polynomial neural network, or called polynomial network classifier (PNC), is a powerful nonlinear classifier that can separate classes of complicated distributions. A method that expands polynomial terms on principal subspace has yielded superior performance. In this paper, we aim to further improve the performance of the subspace-featurebased PNC. In the framework of discriminative feature...
متن کاملSalient feature and reliable classifier selection for facial expression classification
A novel facial expression classification (FEC) method is presented and evaluated. The classification process is decomposed into multiple two-class classification problems, a choice that is analytically justified, and unique sets of features are extracted for each classification problem. Specifically, for each two-class problem, an iterative feature selection process that utilizes a class separa...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Mathematical Problems in Engineering
سال: 2015
ISSN: 1024-123X,1563-5147
DOI: 10.1155/2015/846895