First IndexFree Manifold RankingBased Image Retrieval with Output Bound Image retrieval keeps attracting a lot of attention from both academic and industry over past years due to its variety of useful applications. Due to the rapid growth of deep learning approaches, more better feature vectors of images could be discovered for improving image retrieval. However, most (if not all) existing deep learning approaches consider the similarity between 2 images locally without considering the similarity among a group of similar images globally, and thus could not return accurate results. In this paper, we study the image retrieval with manifold ranking (MR) which considers both the local similarity and the global similarity, which could give more accurate results. However, existing bestknown algorithms have one of the following issues: (1) They require a bulky index, (2) some of them do not have any theoretical bound on the output, and (3) some of them are timeconsuming. Motivated by this, we propose an algorithm, namely Monte Carlobased MR (MCMR) for image retrieval, which does not have the above issues. We are the first one to propose an indexfree manifold rankingbased image retrieval with the output theoretical bound. Lastly, our experiments show that MCMR outperforms existing algorithms by up to 4 orders of magnitude in terms of query time.

Multiple Query ContentBased Image Retrieval Using Relevance Feature Weight Learning We propose a novel multiple query retrieval approach, named weightlearner, which relies on visual feature discrimination to estimate the distances between the query images and images in the database. For each query image, this discrimination consists of learning, in an unsupervised manner, the optimal relevance weight for each visual featuredescriptor. These feature relevance weights are designed to reduce the semantic gap between the extracted visual features and the userxe2x80x99s highlevel semantics. We mathematically formulate the proposed solution through the minimization of some objective functions. This optimization aims to produce optimal feature relevance weights with respect to the user query. The proposed approach is assessed using an image collection from the Corel database.