Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study

Abstract

Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Mover's Distance and the χ 2 distance. We first evaluate the performance of our approach with different keypoint detectors and descriptors, as well as different kernels and classifiers. We then conduct a comparative evaluation with several state-of-the-art recognition methods on four texture and five object databases. On most of these databases, our implementation exceeds the best reported results and achieves comparable performance on the rest. Finally, we investigate the influence of background correlations on recognition performance via extensive tests on the PASCAL database, for which ground-truth object localization information is available. Our experiments demonstrate that image representations based on distributions of local features are surprisingly effective for classification of texture and object images under challenging real-world conditions, including significant intra-class variations and substantial background clutter.

Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach

“…This is an analogue of the human visual pattern recognition system, which is extremely proficient at identifying the damage patterns regardless of the scale and complexity of the scene. In the field of computer vision, various methods have been reported for pattern recognition tasks in various applications, such as object categorization, face recognition, and natural scene classification [12][13][14]. These methods are mostly based on supervised learning approaches, which work well for conventional image classification applications.…”

“…However, the overall performance of the learning approach completely depends on the discriminative power of the image descriptors (features) considered for the classification [15]. Generally, images are described through either global (e.g., textures) or local features, like point descriptors such as Scale Invariant Feature Transform (SIFT) [13,16]. However, most global features are very sensitive to scale and clutter [17].…”

Coloring Action Recognition in Still Images

“…In all cases, the final image representation is based on a spatial pyramid of three levels (1 × 1, 2 × 2, and 3 × 3), yielding a total of 14 cells (Lazebnik et al, 2006). For classification, we use a nonlinear SVM with a χ 2 kernel (Zhang et al, 2007). For classifier fusion we use the addition of different kernel responses since in all our experiments it was shown to provide superior results compared to multiplication of kernels.…”

Pedestrian Density Estimation by a Weighted Bag of Visual Words Model

“…In recent years significant progress has been made in the field of object detection and recognition [1]. While standard "scanning-window" methods attempt to localize objects independently, several recent approaches extend this work and exploit scene context as well as relations among objects for improved object detection [2]. Related ideas have been investigated for human motion analysis where incorporating scene-level and behavioral factors effecting the spatial arrangement and movement of people have been shown beneficial for achieving improved detection and tracking accuracy.…”