An Automated System for Garment Texture Design Class Identification

Abstract

Automatic identification of garment design class might play an important role in the garments and fashion industry. To achieve this, essential initial works are found in the literature. For example, construction of a garment database, automatic segmentation of garments from real life images, categorizing them into the type of garments such as shirts, jackets, tops, skirts, etc. It is now essential to find a system such that it will be possible to identify the particular design (printed, striped or single color) of garment product for an automated system to recommend the garment trends. In this paper, we have focused on this specific issue and thus propose two new descriptors namely Completed CENTRIST (cCENTRIST) and Ternary CENTRIST (tCENTRIST). To test these descriptors, we used two different publically available databases. The experimental results of these databases demonstrate that both cCENTRIST and tCENTRIST achieve nearly about 3% more accuracy than the existing state-of-the art methods.

Face Liveness Detection Using Dynamic Local Ternary Pattern (DLTP)

“…Secondly, for dealing with different characteristics of textures [30] for spoof attacks, this experiment is conducted to compare the performance of the DLTP method with the LTP on four spoof databases: NUAA, Replay-Attack, CASIA and the UPM spoof database. The corresponding results of LTP and DLTP presented in Table 3 by utilizing the test dataset that is already mentioned in Table 1 for all the databases.…”

“…Firstly, we performed the validation by utilizing the developing dataset for tuning the classifier and selecting the best threshold value for the LTP texture descriptor in the application of face liveness detection. The rate of face liveness detection with LTP on development sets in all four face spoof databases are shown in Table 2 Secondly, for dealing with different characteristics of textures [30] for spoof attacks, this experiment is conducted to compare the performance of the DLTP method with the LTP on four spoof databases: NUAA, Replay-Attack, CASIA and the UPM spoof database. The corresponding results of LTP and DLTP presented in Table 3 by utilizing the test dataset that is already mentioned in Table 1 for all the databases.…”

Remote Sensing Image Scene Classification

“…Deep learning has come with a revolutionary change in the field of machine learning. Accuracy of different datasets jumped after applying deep learning approach [21][22][23]. The typical Convolutional Neural Networks (ConvNet) including Alexnet [7], VGGNET [8], GoogleNet [9] has been applied for scene classification purpose.…”

Garments Texture Design Class Identification Using Deep Convolutional Neural Network

“…LTP [6], Completed Local Binary Pattern (CLBP) [7] can handle this issue more accurately. Between these two methods, CLBP is better choice because this method is rotation invariant [8]. CENTRIST [1] has gain popularity by incorporating Spatial Pyramid (SP) structure.…”

“…CENTRIST [1] has gain popularity by incorporating Spatial Pyramid (SP) structure. But, most recently Completed CENTRIST (cCENTRIST) and Ternary CENTRIST (tCENTRIST) [8] gained high accuracies for garments design classification. Although several Hand-Engineered feature extraction approaches exist for garments design classification, deep learning [9] is rarely used in this field.…”