Texture-Aware Superpixel Segmentation

Most superpixel algorithms compute a trade-off between spatial and color features at the pixel level. Hence, they may need fine parameter tuning to balance the two measures, and highly fail to group pixels with similar local texture properties. In this paper, we address these issues with a new Texture-Aware SuperPixel (TASP) method. To accurately segment textured and smooth areas, TASP automatically adjusts its spatial constraint according to the local feature variance. Then, to ensure texture homogeneity within superpixels, a new pixel to superpixel patch-based distance is proposed. TASP outperforms the segmentation accuracy of the state-of-the-art methods on texture and also natural color image datasets

Audiovisual Saliency Prediction in Uncategorized Video Sequences based on Audio-Video Correlation

Substantial research has been done in saliency modeling to develop intelligent machines that can perceive and interpret their surroundings. But existing models treat videos as merely image sequences excluding any audio information, unable to cope with inherently varying content. Based on the hypothesis that an audiovisual saliency model will be an improvement over traditional saliency models for natural uncategorized videos, this work aims to provide a generic audio/video saliency model augmenting a visual saliency map with an audio saliency map computed by synchronizing low-level audio and visual features. The proposed model was evaluated using different criteria against eye fixations data for a publicly available DIEM video dataset. The results show that the model outperformed two state-of-the-art visual saliency models.Comment: 9 pages, 2 figures, 4 table

Feature extraction for human action recognition based on saliency map

Human Action Recognition (HAR) plays an important role in computer vision for the interaction between human and environments which has been widely used in many applications. The focus of the research in recent years is the reliability of the feature extraction to achieve high performance with the usage of saliency map. However, this task is challenging where problems are faced during human action detection when most of videos are taken with cluttered background scenery and increasing the difficulties to detect or recognize the human action accurately due to merging effects and different level of interest. In this project, the main objective is to design a model that utilizes feature extraction with optical flow method and edge detector. Besides, the accuracy of the saliency map generation is needed to improve with the feature extracted to recognize various human actions. For feature extraction, motion and edge features are proposed as two spatial-temporal cues that using edge detector and Motion Boundary Histogram (MBH) descriptor respectively. Both of them are able to describe the pixels with gradients and other vector components. In addition, the features extracted are implemented into saliency computation using Spectral Residual (SR) method to represent the Fourier transform of vectors to log spectrum and eliminating excessive noises with filtering and data compressing. Computation of the saliency map after obtaining the remaining salient regions are combined to form a final saliency map. Simulation result and data analysis is done with benchmark datasets of human actions using Matlab implementation. The expectation for proposed methodology is to achieve the state-of-art result in recognizing the human actions

Unsupervised Superpixel Generation using Edge-Sparse Embedding

Partitioning an image into superpixels based on the similarity of pixels with respect to features such as colour or spatial location can significantly reduce data complexity and improve subsequent image processing tasks. Initial algorithms for unsupervised superpixel generation solely relied on local cues without prioritizing significant edges over arbitrary ones. On the other hand, more recent methods based on unsupervised deep learning either fail to properly address the trade-off between superpixel edge adherence and compactness or lack control over the generated number of superpixels. By using random images with strong spatial correlation as input, \ie, blurred noise images, in a non-convolutional image decoder we can reduce the expected number of contrasts and enforce smooth, connected edges in the reconstructed image. We generate edge-sparse pixel embeddings by encoding additional spatial information into the piece-wise smooth activation maps from the decoder's last hidden layer and use a standard clustering algorithm to extract high quality superpixels. Our proposed method reaches state-of-the-art performance on the BSDS500, PASCAL-Context and a microscopy dataset