326 research outputs found
DISC: Deep Image Saliency Computing via Progressive Representation Learning
Salient object detection increasingly receives attention as an important
component or step in several pattern recognition and image processing tasks.
Although a variety of powerful saliency models have been intensively proposed,
they usually involve heavy feature (or model) engineering based on priors (or
assumptions) about the properties of objects and backgrounds. Inspired by the
effectiveness of recently developed feature learning, we provide a novel Deep
Image Saliency Computing (DISC) framework for fine-grained image saliency
computing. In particular, we model the image saliency from both the coarse- and
fine-level observations, and utilize the deep convolutional neural network
(CNN) to learn the saliency representation in a progressive manner.
Specifically, our saliency model is built upon two stacked CNNs. The first CNN
generates a coarse-level saliency map by taking the overall image as the input,
roughly identifying saliency regions in the global context. Furthermore, we
integrate superpixel-based local context information in the first CNN to refine
the coarse-level saliency map. Guided by the coarse saliency map, the second
CNN focuses on the local context to produce fine-grained and accurate saliency
map while preserving object details. For a testing image, the two CNNs
collaboratively conduct the saliency computing in one shot. Our DISC framework
is capable of uniformly highlighting the objects-of-interest from complex
background while preserving well object details. Extensive experiments on
several standard benchmarks suggest that DISC outperforms other
state-of-the-art methods and it also generalizes well across datasets without
additional training. The executable version of DISC is available online:
http://vision.sysu.edu.cn/projects/DISC.Comment: This manuscript is the accepted version for IEEE Transactions on
Neural Networks and Learning Systems (T-NNLS), 201
Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance
Fine-grained multi-label classification models have broad applications in
Amazon production features, such as visual based label predictions ranging from
fashion attribute detection to brand recognition. One challenge to achieve
satisfactory performance for those classification tasks in real world is the
wild visual background signal that contains irrelevant pixels which confuses
model to focus onto the region of interest and make prediction upon the
specific region. In this paper, we introduce a generic semantic-embedding deep
neural network to apply the spatial awareness semantic feature incorporating a
channel-wise attention based model to leverage the localization guidance to
boost model performance for multi-label prediction. We observed an Avg.relative
improvement of 15.27% in terms of AUC score across all labels compared to the
baseline approach. Core experiment and ablation studies involve multi-label
fashion attribute classification performed on Instagram fashion apparels'
image. We compared the model performances among our approach, baseline
approach, and 3 alternative approaches to leverage semantic features. Results
show favorable performance for our approach
A combined approximating and interpolating subdivision scheme with C2 continuity
AbstractIn this paper a combined approximating and interpolating subdivision scheme is presented. The relationship between approximating subdivision and interpolating subdivision is derived by directly performing operations on geometric rules. The behavior of the limit curve produced by our combined subdivision scheme is analyzed by the Laurent polynomial and attains C2 degree of smoothness. Furthermore, a non-uniform combined subdivision with shape control parameters is introduced, which allows a different tension value for every edge of the original control polygon
High-order wavelet reconstruction for multi-scale edge aware tone mapping
This paper presents a High Order Reconstruction (HOR) method for improved multi-scale edge aware tone mapping. The study aims to contribute to the improvement of edge-aware techniques for smoothing an input image, while keeping its edges intact. The proposed HOR methods circumvent limitations of the existing state of the art methods, e.g., altering the image structure due to changes in contrast; remove artefacts around edges; as well as reducing computational complexity in terms of implementation and associated computational costs. In particular, the proposed method aims at reducing the changes in the image structure by intrinsically enclosing an edge-stop mechanism whose computational cost is comparable to the state-of-the-art multi-scale edge aware techniques
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