Spatial Pyramid Kernel Research Articles

Landmark recognition with sparse representation classification and extreme learning machine

Along with the rapid development of intelligent mobile terminals, applications on landmark recognition attract increasingly attentions by world wide researchers in the past several years. Although promising achievements have been presented, designing a robust recognition system with an accurate recognition rate and fast response speed is still challenging. To address these issues, we propose a novel landmark recognition algorithm in this paper using the spatial pyramid kernel based bag-of-words (SPK-BoW) histogram approach with the feedforward artificial neural networks (FNN) and the sparse representation classifier (SRC). In the proposed algorithm, the SPK-BoW approach is first employed to extract features and construct an overcomplete dictionary for landmark image representation. Then, the FNN trained with the extreme learning machine (ELM) algorithm combined with the SRC is implemented for landmark image recognition. We conduct experiments using the Nanyang Technological University (NTU) campus landmark database to show that the proposed method achieves a high recognition rate than ELM and a lower response time than the sparse representation technique.

Visual Sentences for Pose Retrieval Over Low-Resolution Cross-Media Dance Collections

We describe a system for matching human posture (pose) across a large cross-media archive of dance footage spanning nearly 100 years, comprising digitized photographs and videos of rehearsals and performances. This footage presents unique challenges due to its age, quality and diversity. We propose a forest-like pose representation combining visual structure (self-similarity) descriptors over multiple scales, without explicitly detecting limb positions which would be infeasible for our data. We explore two complementary multi-scale representations, applying passage retrieval and latent Dirichlet allocation (LDA) techniques inspired by the text retrieval domain, to the problem of pose matching. The result is a robust system capable of quickly searching large cross-media collections for similarity to a visually specified query pose. We evaluate over a cross-section of the UK National Research Centre for Dance's (UK-NRCD), and the Siobhan Davies Replay's (SDR) digital dance archives, using visual queries supplied by dance professionals. We demonstrate significant performance improvements over two base-lines: classical single and multi-scale bag of visual words (BoVW) and spatial pyramid kernel (SPK) matching .

Product-Image Classification with Support Vector Machine

SVMs with kernel have been established with good generalization capabilities. This paper proposed a supervised product-image classification method based on SVM and Pyramid Histogram of words(PHOW). We tested several kernel functions on PI100 (Microsoft product-image dataset), such as linear, Radial Basis, Chi-square, histogram intersection and spatial pyramid kernel. Experimental results showed the effectiveness of our algorithm.

Efficient Subwindow Search: A Branch and Bound Framework for Object Localization

Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To estimate the object's location, one can take a sliding window approach, but this strongly increases the computational cost because the classifier or similarity function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branch and bound scheme that allows efficient maximization of a large class of quality functions over all possible subimages. It converges to a globally optimal solution typically in linear or even sublinear time, in contrast to the quadratic scaling of exhaustive or sliding window search. We show how our method is applicable to different object detection and image retrieval scenarios. The achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task, such as SVMs with a spatial pyramid kernel or nearest-neighbor classifiers based on the \chi;2 distance. We demonstrate state-of-the-art localization performance of the resulting systems on the UIUC Cars data set, the PASCAL VOC 2006 data set, and in the PASCAL VOC 2007 competition.