Object-Centric Open-Vocabulary Image-Retrieval with Aggregated Features

The task of open-vocabulary object-centric image retrieval involves the retrieval of images containing a specified object of interest, delineated by an open-set text query. As working on large image datasets becomes standard, solving this task efficiently has gained significant practical importance. Applications include targeted performance analysis of retrieved images using ad-hoc queries and hard example mining during training. Recent advancements in contrastive-based open vocabulary systems have yielded remarkable breakthroughs, facilitating large-scale open vocabulary image retrieval. However, these approaches use a single global embedding per image, thereby constraining the system's ability to retrieve images containing relatively small object instances. Alternatively, incorporating local embeddings from detection pipelines faces scalability challenges, making it unsuitable for retrieval from large databases. In this work, we present a simple yet effective approach to object-centric open-vocabulary image retrieval. Our approach aggregates dense embeddings extracted from CLIP into a compact representation, essentially combining the scalability of image retrieval pipelines with the object identification capabilities of dense detection methods. We show the effectiveness of our scheme to the task by achieving significantly better results than global feature approaches on three datasets, increasing accuracy by up to 15 mAP points. We further integrate our scheme into a large scale retrieval framework and demonstrate our method's advantages in terms of scalability and interpretability.Comment: BMVC 202

Uncertainty Analysis for Spatial Image Extractions in the context of Ontology and Fuzzy C-Means Algorithm

This paper emphasis on spatial feature extractions and selection techniques adopted in content based image retrieval that uses the visual content of a still image to search for similar images in large scale image databases, according to a user2019;s interest. The content based image retrieval problem is motivated by the need to search the exponentially increasing space of image databases efficiently and effectively. It is also possible to classify the remotely sensed image to represent the specific feature of the target images. In this research we first imposed the Fuzzy C-means algorithm to our sample image and observed its value. After getting the experimental result from Fuzzy C-means we have had designed Ontological Matching algorithm which aftereffect better than the previous one. We have had espy that our Ontological Matching algorithm is twenty (20%) percent better than Fuzzy C-means algorithm

An Image Indexing and Region based on Color and Texture

From the previous decade, the enormous rise of the internet has tremendously maximized the amount image databases obtainable. This image gathering such as art works, satellite and medicine is fascinating ever more customers in numerous application domains. The work on image retrieval primarily focuses on efficient and effective relevant images from huge and varied image gatherings which is further becoming more fascinating and exciting. In this paper, the author suggested an effective approach for approximating large-scale retrieval of images through indexing. This approach primarily depends on the visual content of the image segment where the segments are obtained through fuzzy segmentation and are demonstrated through high-frequency sub-band wavelets. Furthermore, owing to the complexity in monitoring large scale information and exponential growth of the processing time, approximate nearest neighbor algorithm is employed to enhance the retrieval speed. Thus, a locality-sensitive hashing using (K-NN Algorithm) is adopted for region-aided indexing technique. Particularly, as the performance of K-NN Approach hinges essentially on the hash function segregating the space, a novel function was uncovered motivated using E8 lattice which could efficiently be amalgamated with multiple probes K-NN Approach and query-adaptive K- NN Approach. To validate the adopted hypothetical selections and to enlighten the efficiency of the suggested approach, a group of experimental results associated to the region-based image retrieval is carried out on the COREL data samples

The Image Shape Spectrum for Image Retrieval

Projet SYNTIMWe present an appearance-based technique for image characterization and retrieval. Our method is translation/rotation and scale- invariant and encodes the significant data in the image without using any segmentation. It is also very well suited to small viewpoint changes and is robust to noise and occlusi on. We present several retrieval examples in large benchmark databases, inclu ding face databases and a database of 3D objects, for which the method reaches an ideal recognition rate

A MEDICAL X-RAY IMAGE CLASSIFICATION AND RETRIEVAL SYSTEM

Medical image retrieval systems have gained high interest in the scientific community due to the advances in medical imaging technologies. The semantic gap is one of the biggest challenges in retrieval from large medical databases. This paper presents a retrieval system that aims at addressing this challenge by learning the main concept of every image in the medical database. The proposed system contains two modules: a classification/annotation and a retrieval module. The first module aims at classifying and subsequently annotating all medical images automatically. SIFT (Scale Invariant Feature Transform) and LBP (Local Binary Patterns) are two descriptors used in this process. Image-based and patch-based features are used as approaches to build a bag of words (BoW) using these descriptors. The impact on the classification performance is also evaluated. The results show that the classification accuracy obtained incorporating image-based integration techniques is higher than the accuracy obtained by other techniques. The retrieval module enables the search based on text, visual and multimodal queries. The text-based query supports retrieval of medical images based on categories, as it is carried out via the category that the images were annotated with, within the classification module. The multimodal query applies a late fusion technique on the retrieval results obtained from text-based and image-based queries. This fusion is used to enhance the retrieval performance by incorporating the advantages of both text-based and content-based image retrieval

MASCOT: a mechanism for attention-based scale-invariant object recognition in images

The efficient management of large multimedia databases requires the development of new techniques to process, characterize, and search for multimedia objects. Especially in the case of image data, the rapidly growing amount of documents prohibits a manual description of the images’ content. Instead, the automated characterization is highly desirable to support annotation and retrieval of digital images. However, this is a very complex and still unsolved task. To contribute to a solution of this problem, we have developed a mechanism for recognizing objects in images based on the query by example paradigm. Therefore, the most salient image features of an example image representing the searched object are extracted to obtain a scale-invariant object model. The use of this model provides an efficient and robust strategy for recognizing objects in images independently of their size. Further applications of the mechanism are classical recognition tasks such as scene decomposition or object tracking in video sequences

Shot-based object retrieval from video with compressed Fisher vectors

This paper addresses the problem of retrieving those shots from a database of video sequences that match a query image. Existing architectures are mainly based on Bag of Words model, which consists in matching the query image with a high-level representation of local features extracted from the video database. Such architectures lack however the capability to scale up to very large databases. Recently, Fisher Vectors showed promising results in large scale image retrieval problems, but it is still not clear how they can be best exploited in video-related applications. In our work, we use compressed Fisher Vectors to represent the video-shots and we show that inherent correlation between video-frames can be proficiently exploited. Experiments show that our proposal enables better performance for lower computational requirements than similar architectures