C++, neural networks and fuzzy logic (2nd ed.)
C++, neural networks and fuzzy logic (2nd ed.)
IEEE Transactions on Pattern Analysis and Machine Intelligence
The psychology of multimedia databases
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Digital Image Processing
K-DIME: An Affective Image Filtering System
IEEE MultiMedia
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
International Journal of Human-Computer Studies
Context-based video retrieval system for the life-log applications
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
The importance of affective quality
Communications of the ACM - Special issue: RFID
Practical experience recording and indexing of Life Log video
CARPE '05 Proceedings of the 2nd ACM workshop on Continuous archival and retrieval of personal experiences
Emotion-Based textile indexing using colors and texture
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Modeling human aesthetic perception of visual textures
ACM Transactions on Applied Perception (TAP)
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We propose a textile indexing system which can classify textile images based on human emotions. The emotions can be regarded as emotional reactions of human beings when they view specific textile images. The evaluation system starts with extracting features of textile images such as colors, texture and patterns using various image processing techniques. The proposed system utilizes both fuzzy rules and neural networks. The fuzzy rules are determined for six emotional features which can be formulated with respect to color and texture. On the other hand, the neural network is used for recognizing patterns which can be used in classifying textile images based on the 4 other emotional features. For the machine learning component of the system, we selected 70 subjects so that they could view and annotate 160 textile images using ten pairs of emotional features. The fuzzy rule based component of the system uses color features and texture features in order to predict six pairs of emotional features such as (warm, cold), (gay, sober), (cheerful, dismal), (light, dark), (strong, weak), and (hard, soft). The neural-network based component of the system can predict four pairs of emotional features such as (natural, unnatural), (dynamic, static), (unstable, stable) and (gaudy, plain). Our experimental results showed that the proposed system was effective for predicting human emotions based on textile images and improving the accuracy of indexing the textile images based on emotional features.