This paper introduces several novel Gabor-based local, shape and color features for image classification. First, a new Gabor-HOG (GHOG) descriptor is proposed for image feature extraction by concatenating the Histograms of Oriented Gradients (HOG) of all the local Gabor filtered images. The GHOG descriptor is then further assessed in six different color spaces to measure classification performa...

The most important bottleneck for facial expression recognition system is recognizing the expression in uncontrolled environments with minimum computational time consumption. This problem has been addressed by combining the robust local texture descriptors which are invariant to illumination effects. In this work, the illumination effects are eliminated by using Weber Local Descriptor (WLD). Ne...

Although the recent progress in deep neural network has led to the development of learnable local feature descriptors, there is no explicit answer for estimation of the necessary size of a neural network. Specifically, the local feature is represented in a low dimensional space, so the neural network should have more compact structure. The small networks required for local feature descriptor le...

Many recent applications using local image features focus on 2D image recognition. Such applications can not distinguish between real objects and photos of objects. In this project, we present a 3D object recognition method using stereo images. Using the 3D information of the objects obtained from stereo images, objects with similar image description but different 3D shapes can be distinguished...

This paper deals with the retrieval of scene-of-crime (or scene) shoeprint images from a reference database of shoeprint images by using a new local feature detector and an improved local feature descriptor. Our approach is based on novel modifications and improvements of a few recent techniques in this area: (1) the scale adapted Harris detector, which is an extension to multi-scale domains of...

Object recognition is an important problem in computer vision, having diverse applications. In this work, we construct an end-to-end scene recognition pipeline consisting of feature extraction, encoding, pooling and classification. Our approach simultaneously utilize global feature descriptors as well as local feature descriptors from images, to form a hybrid feature descriptor corresponding to...

In this paper, we introduce a shape-based, time-scale invariant feature descriptor for 1-D sensor signals. The time-scale invariance of the feature allows us to use feature from one training event to describe events of the same semantic class which may take place over varying time scales such as walking slow and walking fast. Therefore it requires less training set. The descriptor takes advanta...

Facial expression recognition (FER) is a crucial technology and a challenging task for human–computer interaction. Previous methods have been using different feature descriptors for FER and there is a lack of comparison study. In this paper, we aim to identify the best features descriptor for FER by empirically evaluating five feature descriptors, namely Gabor, Haar, Local Binary Pattern (LBP),...