Top down image semantics play a major role in predicting where people look in images. Present state-of-the-art approaches to model human visual attention incorporate high level object detections signifying top down image semantics in a separate channel along with other bottom up saliency channels. However, multiple objects in a scene are competing to attract our attention and this interaction is ignored in current models. To overcome this limitation, we propose a novel object context based visual attention model which incorporates the co-occurrence of multiple objects in a scene for visual attention modeling. The proposed regression based algorithm uses several high level object detectors for faces, people, cars, text and understands how their joint presence affects visual attention. Experimental results on the MIT eye tracking dataset demonstrates that the proposed method outperforms other state-of-the-art visual attention models.

In this paper, we present a novel video fingerprinting algorithm which leverages the concept of perceptual similarity between different video sequences. Inspired by the popular structural similarity (SSIM) index, we quantify the perceptual similarity between different video sequences by proposing a perceptual distance metric (PDM) which is utilized in the matching stage of our proposed video fingerprinting algorithm. PDM requires very simple features, viz., block means and therefore has extremely low complexity in both the feature extraction part, as well as during the matching stage. We also show how to use an order statistic in the proposed distance measure to improve the system performance for localized block-based artifacts such as the logo artifact. Simulation results for the proposed fingerprinting algorithm show significant gains over other video fingerprinting techniques on different video datasets for numerous heavy video artifacts.

A common challenge when dealing with heterogenous tasks such as face expression analysis, face and object recognition is high dimensionality and extreme appearance variations within each class. To handle such scenarios, we formulate a supervised Non-negative Matrix Factorization (NMF) based subspace learning technique that simultaneously preserves the intra-class regression information (local) and enhances inter-class discrimination (global) in the low dimensional embedding. Our method leverages the multi-dimensional image labels that quantify the within class regression to learn the subspaces for recognition. In addition, our formulation includes a novel multi-output regression based NMF algorithm.

Our paper presents a novel high dimensional probability density estimation technique using any dimensionality reduction method. Our method first performs subspace reduction using any matrix factorization algorithm and estimates the density in the low-dimensional space using sample-point variable bandwidth kernel density estimation. Subsequently, the high dimensional density is approximated from the low dimensional density parameters. The reconstruction error due to dimensionality reduction process is also modeled in a principled and efficient manner to obtain the high dimensional density estimate. We show the effectiveness of our technique by using two popular dimensionality reduction tools, principal component analysis and non-negative matrix factorization. This technique is applied to AT&T, Yale, Pointing'04 and CMU-PIE face recognition datasets and improved performance compared to other dimensionality reduction and density estimation algorithms is obtained.

Non-negative matrix factorization (NMF) is an excellent tool for unsupervised parts-based learning, but proves to be ineffective when parts of a whole follow a specific pattern. Analyzing such local changes is particularly important when studying anatomical transformations. We propose a supervised method that incorporates a regression constraint into the NMF framework and learns maximally changing parts in the basis images, called Regression based NMF (RNMF). The algorithm is made robust against outliers by learning the distribution of the input manifold space, where the data resides. One of our main goals is to achieve good region localization. By incorporating a gradient smoothing and independence constraint into the factorized bases, contiguous local regions are captured. We apply our technique to a synthetic dataset and structural MRI brain images of subjects with varying ages. RNMF finds the localized regions which are expected to be highly changing over age to be manifested in its significant basis and it also achieves the best performance compared to other statistical regression and dimensionality reduction techniques.

Non-negative matrix factorization (NMF) has proven to be useful in image classification applications such as face recognition. We propose a novel discriminative basis selection method for classification of image categories based on the popular term frequency-inverse document frequency (TF-IDF) weight used in information retrieval. We extend the algorithm to incorporate color, and overcome the drawbacks of using unaligned images. Our method is able to choose visually significant bases which best discriminate between categories and thus prune the classification space to increase correct classifications. We apply our technique to ETH-80, a standard image classification benchmark dataset. Our results show that our algorithm outperforms other state-of-the-art techniques.