Illumination Estimation from Dichromatic Planes

Adopting the dichromatic reflection model under the assumption of neutral interface reflection, the color of the illuminating light can be estimated by intersecting the planes that the color response of two or more different materials describe. From the color response of any given region, most approaches estimate a single plane on the assumption that only a single material is imaged. This assumption, however, is often violated in cluttered scenes. In this paper, rather than a single planar model, several coexisting planes are used to explain the observed color response. In estimating the illuminant, a set of candidate lights is assessed for goodness of fit given the assumed number of coexisting planes. The candidate light giving the minimum error fit is then chosen as representative of the scene illuminant. The performance of the proposed approach is explored on real images

Specularity Removal from Imaging Spectroscopy Data via Entropy Minimisation

In this paper, we present a method to remove specularities from imaging spectroscopy data. We do this by making use of the dichromatic model so as to cast the problem in a linear regression setting. We do this so as to employ the average radiance for each pixel as a means to map the spectra onto a two-dimensional space. This permits the use of an entropy minimisation approach so as to recover the slope of a line described by a linear regressor. We show how this slope can be used to recover the specular coefficient in the dichromatic model and provide experiments on real-world imaging spectroscopy data. We also provide comparison with an alternative and effect a quantitative analysis that shows our method is robust to changes the degree of specularity of the image or the location of the light source in the scene

Summarizing Videos with Attention

In this work we propose a novel method for supervised, keyshots based video summarization by applying a conceptually simple and computationally efficient soft, self-attention mechanism. Current state of the art methods leverage bi-directional recurrent networks such as BiLSTM combined with attention. These networks are complex to implement and computationally demanding compared to fully connected networks. To that end we propose a simple, self-attention based network for video summarization which performs the entire sequence to sequence transformation in a single feed forward pass and single backward pass during training. Our method sets a new state of the art results on two benchmarks TvSum and SumMe, commonly used in this domain.Comment: Presented at ACCV2018 AIU2018 worksho

Interactive image segmentation based on level sets of probabilities

In this paper, we present a robust and accurate algorithm for interactive image segmentation. The level set method is clearly advantageous for image objects with a complex topology and fragmented appearance. Our method integrates discriminative classification models and distance transforms with the level set method to avoid local minima and better snap to true object boundaries. The level set function approximates a transformed version of pixelwise posterior probabilities of being part of a target object. The evolution of its zero level set is driven by three force terms, region force, edge field force, and curvature force. These forces are based on a probabilistic classifier and an unsigned distance transform of salient edges. We further propose a technique that improves the performance of both the probabilistic classifier and the level set method over multiple passes. It makes the final object segmentation less sensitive to user interactions. Experiments and comparisons demonstrate the effectiveness of our method. © 2012 IEEE.published_or_final_versio

Objective quality prediction of image retargeting algorithms

Quality assessment of image retargeting results is useful when comparing different methods. However, performing the necessary user studies is a long, cumbersome process. In this paper, we propose a simple yet efficient objective quality assessment method based on five key factors: i) preservation of salient regions; ii) analysis of the influence of artifacts; iii) preservation of the global structure of the image; iv) compliance with well-established aesthetics rules; and v) preservation of symmetry. Experiments on the RetargetMe benchmark, as well as a comprehensive additional user study, demonstrate that our proposed objective quality assessment method outperforms other existing metrics, while correlating better with human judgements. This makes our metric a good predictor of subjective preference

Image Classification Using Bag-of-Visual-Words Model

Recently, with the explosive growth of digital technologies, there has been a rapid proliferation of the size of image collection. The technique of supervised image clas sification has been widely applied in many domains in order to organize, search, and retrieve images. However, the traditional feature extraction approaches yield the poor classification accuracy. Therefore, the Bag-of-visual-words model, inspired by Bag-of Words model in document classification, was used to present images with the local descriptors for image classification, and also it performs well in some fields. This research provides the empirical evidence to prove that the BoVW model outperforms the traditional feature extraction approaches for both binary image clas sification and multi-class image classification. Furthermore, the research reveals that the size of the visual vocabulary during the process of building BoVW model impact on the accuracy results of image classification