In this paper, we address the problem of content-based image retrieval (CBIR) by learning images representations based on activations a Convolutional Neural Network. We propose an end-to-end trainable network architecture that exploits novel multi-scale local pooling aggregation layer NetVLAD (Arandjelovic et al in Proceedings IEEE conference computer vision and pattern recognition CVPR, NetVLA...

Zur Beantwortung der Frage, warum Bildersuchen auf Schlüsselwörtern basieren, statt auf den bildimmanenten Informationen, werden Repräsentationsmöglichkeiten für Bildinformationen vorgestellt, die Personen in die Lage versetzen, Anfragen in Form von Bildinformationen statt Schlüsselwörtern zu formulieren. Die Bildinformationen sollen in einer Weise interpretiert werden, die dem subjektiven Empf...

Standardised image databases or rather the lack of them are one of the main weaknesses in the field of content based image retrieval (CBIR). Authors often use their own images or do not specify the source of their datasets. Naturally this makes comparison of results somewhat difficult. While a first approach towards a common colour image set has been taken by the MPEG 7 committee their database...

Advances in content-based image retrieval(CBIR)lead to numerous efficient techniques for retrieving images based on their content features, such as colours, textures and shapes. However, CBIR to date has been mainly focused on a centralised environment, ignoring the rapidly increasing image collection in the world, the images on the Web. In this paper, we study the problem of distributed CBIR i...

In this paper, we present the main features of VISTO (Vector Image Serach TOol), a new Content-Based Image Retrieval (CBIR) system for vector images. Though unsuitable for photorealistic imagery, vector graphics are continually becoming more advanced and diffused. In fact, vector images are made up of individual, scalable objects defined by mathematical equations rather than pixels as in the ca...

This study aims to find more effective methods for collection-specific CBIR. A lot of work has been done in trying to adapt a system by user feedback, in this study we aim to adapt CBIR systems for specific image collections in an automated manner. Independent Component Analysis (ICA), a high order statistical technique, is used to extract Independent Component Filters (ICF) from image sets. As...

A linear search in a Content-Based Image Retrieval (CBIR) system is time consuming. Like any database system an indexing technique is mandatory for the system to be efficient. This paper studies the application of clustering algorithms to a Fuzzy Hamming Distance based CBIR system for building the image index. The study shows good results using complete linkage agglomerative clustering.

Content Based Image Retrieval (CBIR) plays very important role in the research field of digital Image processing. DIP deals with manipulation of digital images through a digital computer. Basically CBIR is responsible for extracting low level features of image like color, texture, shape and similarity measures for the comparison of different images. And after that retrieve the similar images us...

A framework for combining object detection techniques with a content-based image retrieval(CBIR) system is discussed. As an example, a special CBIR system which focuses on human faces as foreground and decides the similarity of images based on background features is presented. This system may be useful in automatically generating albums from consumer photos.

Content-based image retrieval (CBIR) is a method for searching that finds related images in medical database. Furthermore, clinical adaptation of CBIR hampered part by contextual gap the disparity among person characterization picture and framework image. This technique makes it tough user to validate fetched are similar query addition only fetches top-ranked ignores low-ranking ones. Visual An...