“Is this document relevant?…probably”: a survey of probabilistic models in information retrieval
ACM Computing Surveys (CSUR)
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unifying Keywords and Visual Contents in Image Retrieval
IEEE MultiMedia
Deformable Contours: Modeling and Extraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
An active learning framework for content-based information retrieval
IEEE Transactions on Multimedia
Joint semantics and feature based image retrieval using relevance feedback
IEEE Transactions on Multimedia
Semantic-based facial expression recognition using analytical hierarchy process
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Applying fuzzy hierarchy multiple attributes to construct an expert decision making process
Expert Systems with Applications: An International Journal
Prototype system for pursuing firm's core capability
Information Systems Frontiers
Hi-index | 12.06 |
In this paper, a new semantic learning method for content-based image retrieval using the analytic hierarchical process (AHP) is proposed. AHP proposed by Satty used a systematical way to solve multi-criteria preference problems involving qualitative data and was widely applied to a great diversity of areas. In general, the interpretations of an image are multiple and hard to describe in terms of low-level features due to the lack of a complete image understanding model. The AHP provides a good way to evaluate the fitness of a semantic description used to interpret an image. According to a predefined concept hierarchy, a semantic vector, consisting of the fitness values of semantic descriptions of a given image, is used to represent the semantic content of the image. Based on the semantic vectors, the database images are clustered. For each semantic cluster, the weightings of the low-level features (i.e. color, shape, and texture) used to represent the content of the images are calculated by analyzing the homogeneity of the class. In this paper, the values of weightings setting to the three low-level feature types are diverse in different semantic clusters for retrieval. The proposed semantic learning scheme provides a way to bridge the gap between the high-level semantic concept and the low-level features for content-based image retrieval. Experimental results show that the performance of the proposed method is excellent when compared with that of the traditional text-based semantic retrieval techniques and content-based image retrieval methods.