Large-scale image retrieval has been shown remarkable potential in real-life applications. The standard approach is based on Inverted Indexing, given images are represented using Bag-of-Words model. However, one major limitation of both Inverted Index and Bag-of-Words presentation is that they ignore spatial information of visual words in image presentation and comparison. As a result, retrieva...

In this paper, for the first time, we introduce a multiple instance (MI) deep hashing technique for learning discriminative hash codes with weak bag-level supervision suited for large-scale retrieval. We learn such hash codes by aggregating deeply learnt hierarchical representations across bag members through a dedicated MI pool layer. For better trainability and retrieval quality, we propose a...

These days automatic image annotation is an important topic and several efforts are made to solve the semantic gap problem which is still an open issue. Also, Content Based Image Retrieval (CBIR) cannot solve this problem. One of the efficient and effective models for solving the semantic gap and visual recognition and retrieval is Bag of Feature (BoF) model which can quantize local visual feat...

We present a novel similarity measure for bag-of-words type large scale image retrieval. The similarity function is learned in an unsupervised manner, requires no extra space over the standard bag-of-words method and is more discriminative than both L2-based soft assignment and Hamming embedding. Experimentally we show that the novel similarity function achieves mean average precision that is s...

Although the CNNs are a very powerful tool for image retrieval, need of training datasets properly adapted to application at hand hinders usefulness such networks, specially since be free noise avoid spoiling learning process. An ad hoc preprocessing dataset mitigate is possible solution, but it usually non-trivial and requires significant human intervention. In this paper, we pave road retriev...