Semantic Concept Co-occurrence Patterns for Image Annotation and Retrieval

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Semantic Concept Co-occurrence Patterns for Image Annotation and Retrieval

Semantic Concept Co-occurrence Patterns for Image Annotation and Retrieval

Abstract of Semantic Concept Co-occurrence Patterns for Image Annotation and Retrieval

Semantic Concept Co-occurrence Patterns for Image Annotation and Retrieval,Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications.Representing images by semantic concepts instead of visual features remains a challenging problem. Generating semantic descriptors manually is not feasible due to the ever-growing number of image collections.
Current machine intelligence and statistical learning techniques for inferring semantic concepts from low-level features struggle in bridging the semantic gap. However, many image-based applications such as retrieval, annotation, recommendation, indexing and ranking, require an effective semantic representation of images.
There is a growing need in automatically inferring concepts from visual properties by learning the correspondence from loosely labeled data.

Conclusions

This Semantic Concept Co-occurrence Patterns for Image Annotation and Retrievalpaper has made a novel contribution to the literature on context-based co-occurrences in computer vision where co-occurrences of concepts are used as contextual cues for improved concept inference.

The approach is tested on recent practical datasets and compared with the state-of-the-art methods. The experimental results convincingly show the following: (a) The importance of the hierarchy of co-occurrence patterns and its representation as a network structure, (b) The effectiveness of the approach for building individual concept inference models and the utilization of co-occurrence patterns for refinement of concept signature as a way to encode both visual and semantic information.