FAemb: A Function Approximation-Based Embedding Method for Image Retrieval
The objective of this paper is to design an embedding method mapping local features describing image (e.g. SIFT) to a higher dimensional representation used for image retrieval problem. By investigating the relationship between the linear approximation of a nonlinear function in high dimensional space and state-of-the-art feature representation used in image retrieval, i.e., VLAD, we first introduce a new approach for the approximation. The embedded vectors resulted by the function approximation process are then aggregated to form a single representation used in the image retrieval framework. The evaluation shows that our embedding method gives a performance boost over the state of the art in image retrieval, as demonstrated by our experiments on the standard public image retrieval benchmarks.
Code (0)
등록된 구현이 없습니다.
Tasks
Image RetrievalRetrievalSimilar Papers 제목 키워드 기반
Embedding based on function approximation for large scale image search
The objective of this paper is to design an embedding method that maps local features describing an image (e.g. SIFT) to a higher dimensional representation useful for the image retrieval problem. First, motivated by the…
Image RetrievalRetrievalDeep Metric Learning using Similarities from Nonlinear Rank Approximations
In recent years, deep metric learning has achieved promising results in learning high dimensional semantic feature embeddings where the spatial relationships of the feature vectors match the visual similarities of the im…
Metric LearningRetrievalA Differentiable Semantic Metric Approximation in Probabilistic Embedding for Cross-Modal Retrieval
Cross-modal retrieval aims to build correspondence between multiple modalities by learning a common representation space. Typically, an image can match multiple texts semantically and vice versa, which significantly incr…
Cross-Modal RetrievalImage-text matchingImage-to-Text RetrievalRetrievalDeep Randomized Ensembles for Metric Learning
Learning embedding functions, which map semantically related inputs to nearby locations in a feature space supports a variety of classification and information retrieval tasks. In this work, we propose a novel, generaliz…
General ClassificationImage RetrievalInformation RetrievalMetric Learning+1Robust and Decomposable Average Precision for Image Retrieval
In image retrieval, standard evaluation metrics rely on score ranking, e.g. average precision (AP). In this paper, we introduce a method for robust and decomposable average precision (ROADMAP) addressing two major challe…
Image RetrievalMetric LearningRetrieval