Image Retrieval And Classification Using Local Feature Vectors
Content Based Image Retrieval(CBIR) is one of the important subfield in the field of Information Retrieval. The goal of a CBIR algorithm is to retrieve semantically similar images in response to a query image submitted by the end user. CBIR is a hard problem because of the phenomenon known as $\textit {semantic gap}$. In this thesis, we aim at analyzing the performance of a CBIR system build using local feature vectors and Intermediate Matching Kernel. We also propose a Two-Step Matching process for reducing the response time of the CBIR systems. Further, we develop a Meta-Learning framework for improving the retrieval performance of these systems. Our results show that the Two-Step Matching process significantly reduces response time and the Meta-Learning Framework improves the retrieval performance by more than two fold. We also analyze the performance of various image classification systems that use different image representations constructed from the local feature vectors.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationContent-Based Image RetrievalGeneral Classificationimage-classificationImage ClassificationImage RetrievalInformation RetrievalMeta-LearningRetrievalSimilar Papers 제목 키워드 기반
Content-based jewellery item retrieval using the local region-based histograms
Jewellery item retrieval is regularly used to find what people want on online marketplaces using a sample query reference image. Considering recent developments, due to the simultaneous nature of various jewelry items, v…
RetrievalImage Retrieval with Fisher Vectors of Binary Features
Recently, the Fisher vector representation of local features has attracted much attention because of its effectiveness in both image classification and image retrieval. Another trend in the area of image retrieval is the…
ClassificationGeneral Classificationimage-classificationImage Classification+2Aggregating Deep Convolutional Features for Image Retrieval
Several recent works have shown that image descriptors produced by deep convolutional neural networks provide state-of-the-art performance for image classification and retrieval problems. It has also been shown that the …
image-classificationImage ClassificationImage RetrievalRetrievalAggregating Local Deep Features for Image Retrieval
Several recent works have shown that image descriptors produced by deep convolutional neural networks provide state-of-the-art performance for image classification and retrieval problems. It also has been shown that the …
image-classificationImage ClassificationImage RetrievalRetrievalEmbedding 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 RetrievalRetrieval