Multi‐level image representation for large‐scale image‐based instance retrieval

Instance-Based Relevance Feedback for Image Retrieval

High retrieval precision in content-based image retrieval can be attained by adopting relevance feedback mechanisms. These mechanisms require that the user judges the quality of the results of the query by marking all the retrieved images as being either relevant or not. Then, the search engine exploits this information to adapt the search to better meet user’s needs. At present, the vast major...

Multilevel image segmentation and object representation for content-based image retrieval

MultiView: Multilevel video content representation and retrieval

In this article, several practical algorithms are proposed to support content-based video analysis, modeling, representation, summarization, indexing, and access. First, a multilevel video database model is given. One advantage of this model is that it provides a reasonable approach to bridging the gap between low-level representative features and high-level semantic concepts from a human point...

Pruning Convolutional Neural Networks for Image Instance Retrieval

In this work, we focus on the problem of image instance retrieval with deep descriptors extracted from pruned Convolutional Neural Networks (CNN). The objective is to heavily prune convolutional edges while maintaining retrieval performance. To this end, we introduce both data-independent and data-dependent heuristics to prune convolutional edges, and evaluate their performance across various c...

Instance Similarity Deep Hashing for Multi-Label Image Retrieval

Hash coding has been widely used in the approximate nearest neighbor search for large-scale image retrieval. Recently, many deep hashing methods have been proposed and shown largely improved performance over traditional featurelearning-based methods. Most of these methods examine the pairwise similarity on the semantic-level labels, where the pairwise similarity is generally defined in a hard-a...