Improving Bags-of-Words model for object categorization

In the past decade, Bags-of-Words (BOW) models have become popular for the task of object recognition, owing to their good performance and simplicity. Some of the most effective recent methods for computer-based object recognition work by detecting and extracting local image features, before quantizing them according to a codebook rule such as k-means clustering, and classifying these with conventional classifiers such as Support Vector Machines and Naive Bayes. In this thesis, a Spatial Object Recognition Framework is presented that consists of the four main contributions of the research. The first contribution, frequent keypoint pattern discovery, works by combining pairs and triplets of frequent keypoints in order to discover intermediate representations for object classes. Based on the same frequent keypoints principle, algorithms for locating the region-of-interest in training images is then discussed. Extensions to the successful Spatial Pyramid Matching scheme, in order to better capture spatial relationships, are then proposed. The pairs frequency histogram and shapes frequency histogram work by capturing more redefined spatial information between local image features. Finally, alternative techniques to Spatial Pyramid Matching for capturing spatial information are presented. The proposed techniques, variations of binned log-polar histograms, divides the image into grids of different scale and different orientation. Thus captures the distribution of image features both in distance and orientation explicitly. Evaluations on the framework are focused on several recent and popular datasets, including image retrieval, object recognition, and object categorization. Overall, while the effectiveness of the framework is limited in some of the datasets, the proposed contributions are nevertheless powerful improvements of the BOW model

Structured learning of metric ensembles with application to person re-identification

Matching individuals across non-overlapping camera networks, known as person re-identification, is a fundamentally challenging problem due to the large visual appearance changes caused by variations of viewpoints, lighting, and occlusion. Approaches in literature can be categoried into two streams: The first stream is to develop reliable features against realistic conditions by combining several visual features in a pre-defined way; the second stream is to learn a metric from training data to ensure strong inter-class differences and intra-class similarities. However, seeking an optimal combination of visual features which is generic yet adaptive to different benchmarks is a unsoved problem, and metric learning models easily get over-fitted due to the scarcity of training data in person re-identification. In this paper, we propose two effective structured learning based approaches which explore the adaptive effects of visual features in recognizing persons in different benchmark data sets. Our framework is built on the basis of multiple low-level visual features with an optimal ensemble of their metrics. We formulate two optimization algorithms, CMCtriplet and CMCstruct, which directly optimize evaluation measures commonly used in person re-identification, also known as the Cumulative Matching Characteristic (CMC) curve.Comment: 16 pages. Extended version of "Learning to Rank in Person Re-Identification With Metric Ensembles", at http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Paisitkriangkrai_Learning_to_Rank_2015_CVPR_paper.html. arXiv admin note: text overlap with arXiv:1503.0154

Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader