Collection Of Images Research Articles

HaLo‐NeRF: Learning Geometry‐Guided Semantics for Exploring Unconstrained Photo Collections

AbstractInternet image collections containing photos captured by crowds of photographers show promise for enabling digital exploration of large‐scale tourist landmarks. However, prior works focus primarily on geometric reconstruction and visualization, neglecting the key role of language in providing a semantic interface for navigation and fine‐grained understanding. In more constrained 3D domains, recent methods have leveraged modern vision‐and‐language models as a strong prior of 2D visual semantics. While these models display an excellent understanding of broad visual semantics, they struggle with unconstrained photo collections depicting such tourist landmarks, as they lack expert knowledge of the architectural domain and fail to exploit the geometric consistency of images capturing multiple views of such scenes. In this work, we present a localization system that connects neural representations of scenes depicting large‐scale landmarks with text describing a semantic region within the scene, by harnessing the power of SOTA vision‐and‐language models with adaptations for understanding landmark scene semantics. To bolster such models with fine‐grained knowledge, we leverage large‐scale Internet data containing images of similar landmarks along with weakly‐related textual information. Our approach is built upon the premise that images physically grounded in space can provide a powerful supervision signal for localizing new concepts, whose semantics may be unlocked from Internet textual metadata with large language models. We use correspondences between views of scenes to bootstrap spatial understanding of these semantics, providing guidance for 3D‐compatible segmentation that ultimately lifts to a volumetric scene representation. To evaluate our method, we present a new benchmark dataset containing large‐scale scenes with ground‐truth segmentations for multiple semantic concepts. Our results show that HaLo‐NeRF can accurately localize a variety of semantic concepts related to architectural landmarks, surpassing the results of other 3D models as well as strong 2D segmentation baselines. Our code and data are publicly available at https://tau‐vailab.github.io/HaLo‐NeRF/.

Double-Clad Antiresonant Hollow-Core Fiber and Its Comparison with Other Fibers for Multiphoton Micro-Endoscopy

Label-free and multiphoton micro-endoscopy can transform clinical histopathology by providing an in situ tool for diagnostic imaging and surgical treatment in diseases such as cancer. Key to a multiphoton imaging-based micro-endoscopic device is the optical fiber, for distortion-free and efficient delivery of ultra-short laser pulses to the sample and effective signal collection. In this work, we study a new hollow-core (air-filled) double-clad anti-resonant fiber (DC-ARF) as a high-performance candidate for multiphoton micro-endoscopy. We compare the fiber characteristics of the DC-ARF with a single-clad anti-resonant fiber (SC-ARF) and a solid core fiber (SCF). In this work, while the DC-ARF and the SC-ARF enable low-loss (<0.2 dBm−1), close to dispersion-free excitation pulse delivery (<10% pulse width increase at 900 nm per 1 m fiber) without any induced non-linearities, the SCF resulted in spectral broadening and pulse-stretching (>2000% of pulse width increase at 900 nm per 1 m fiber). An ideal optical fiber endoscope needs to be several meters long and should enable both excitation and collection through the fiber. Therefore, we performed multiphoton imaging on endoscopy-compatible 1 m and 3 m lengths of fiber in the back-scattered geometry, wherein the signals were collected either directly (non-descanned detection) or through the fiber (descanned detection). Second harmonic images were collected from barium titanate crystals as well as from biological samples (mouse tail tendon). In non-descanned detection conditions, the ARFs outperformed the SCF by up to 10 times in terms of signal-to-noise ratio of images. Significantly, only the DC-ARF, due to its high numerical aperture (NA) of 0.45 and wide-collection bandwidth (>1 µm), could provide images in the de-scanned detection configuration desirable for endoscopy. Thus, our systematic characterization and comparison of different optical fibers under different image collection configurations, confirms and establishes the utility of DC-ARFs for high-performing label-free multiphoton imaging-based micro-endoscopy.

Image Separation and Sharing Application

A state-of-the-art project created as part of a program is the Image Separation and Sharing Application, which aims to transform the way A cutting-edge project developed as part of a program is the Image Separation and Sharing Application. The users interact with their image collections. Image separation and sharing Applications help users leverage facial recognition and machine learning techniques to build a smooth and effective image separation system. Key Words: image separation, image sharing, machine learning, user interface, Face net library, Face recognition

Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials

BackgroundDuctal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal carcinoma In Situ is safe (E. S. Hwang et al., BMJ Open, 9: e026797, 2019, A. Francis et al., Eur J Cancer. 51: 2296–2303, 2015, Chizuko Kanbayashi et al. The international collaboration of active surveillance trials for low-risk DCIS (LORIS, LORD, COMET, LORETTA),  L. E. Elshof et al., Eur J Cancer, 51, 1497–510, 2015). Low-risk is defined as grade I or II DCIS. Because DCIS grade is a major eligibility criteria in these trials, it would be very helpful to assess DCIS grade on mammography, informed by grade assessed on DCIS histopathology in pre-surgery biopsies, since surgery will not be performed on a significant number of patients participating in these trials.ObjectiveTo assess the performance and clinical utility of a convolutional neural network (CNN) in discriminating high-risk (grade III) DCIS and/or Invasive Breast Cancer (IBC) from low-risk (grade I/II) DCIS based on mammographic features. We explored whether the CNN could be used as a decision support tool, from excluding high-risk patients for active surveillance.MethodsIn this single centre retrospective study, 464 patients diagnosed with DCIS based on pre-surgery biopsy between 2000 and 2014 were included. The collection of mammography images was partitioned on a patient-level into two subsets, one for training containing 80% of cases (371 cases, 681 images) and 20% (93 cases, 173 images) for testing. A deep learning model based on the U-Net CNN was trained and validated on 681 two-dimensional mammograms. Classification performance was assessed with the Area Under the Curve (AUC) receiver operating characteristic and predictive values on the test set for predicting high risk DCIS-and high-risk DCIS and/ or IBC from low-risk DCIS.ResultsWhen classifying DCIS as high-risk, the deep learning network achieved a Positive Predictive Value (PPV) of 0.40, Negative Predictive Value (NPV) of 0.91 and an AUC of 0.72 on the test dataset. For distinguishing high-risk and/or upstaged DCIS (occult invasive breast cancer) from low-risk DCIS a PPV of 0.80, a NPV of 0.84 and an AUC of 0.76 were achieved.ConclusionFor both scenarios (DCIS grade I/II vs. III, DCIS grade I/II vs. III and/or IBC) AUCs were high, 0.72 and 0.76, respectively, concluding that our convolutional neural network can discriminate low-grade from high-grade DCIS.

Robust GAN-Based CNN Model as Generative AI Application for Deepfake Detection

One of the most well-known generative AI models is the Generative Adversarial Network (GAN), which is frequently employed for data generation or augmentation. In this paper a reliable GAN-based CNN deepfake detection method utilizing GAN as an augmentation element is implemented. It aims to give the CNN model a big collection of images so that it can train better with the intrinsic qualities of the images. The major objective of this research is to show how GAN innovations have enhanced and increased the use of generative AI principles, particularly in fake image classification called Deepfakes that poses concerns about misrepresentation and individual privacy.  For identifying these fake photos more synthetic images are created using the GAN model that closely resemble the training data.  It has been observed that GAN-augmented datasets can improve the robustness and generality of CNN-based detection models, which correctly identify between real and false images by 96.35%.

EndoViT: pretraining vision transformers on a large collection of endoscopic images.

Automated endoscopy video analysis is essential for assisting surgeons during medical procedures, but it faces challenges due to complex surgical scenes and limited annotated data. Large-scale pretraining has shown great success in natural language processing and computer vision communities in recent years. These approaches reduce the need for annotated data, which is of great interest in the medical domain. In this work, we investigate endoscopy domain-specific self-supervised pretraining on large collections of data. To this end, we first collect Endo700k, the largest publicly available corpus of endoscopic images, extracted from nine public Minimally Invasive Surgery (MIS) datasets. Endo700k comprises more than 700,000 images. Next, we introduce EndoViT, an endoscopy-pretrained Vision Transformer (ViT), and evaluate it on a diverse set of surgical downstream tasks. Our findings indicate that domain-specific pretraining with EndoViT yields notable advantages in complex downstream tasks. In the case of action triplet recognition, our approach outperforms ImageNet pretraining. In semantic segmentation, we surpass the state-of-the-art (SOTA) performance. These results demonstrate the effectiveness of our domain-specific pretraining approach in addressing the challenges of automated endoscopy video analysis. Our study contributes to the field of medical computer vision by showcasing the benefits of domain-specific large-scale self-supervised pretraining for vision transformers. We release both our code and pretrained models to facilitate further research in this direction: https://github.com/DominikBatic/EndoViT .

Performance Evaluation of Extra Trees Classifier by using CPU Parallel and Non-Parallel Processing

This research delves into assessing the performance of the Extra Trees Classifier, specifically examining the influence of CPU parallel processing on classification accuracy and computational efficiency. Fashion MNIST, a collection of grayscale images representing clothing items, serves as the foundational dataset for this study. Two variations of the Extra Trees Classifier are implemented: one configured without CPU parallel processing and another utilizing maximum CPU cores for parallel execution. The primary evaluation metrics include accuracy measurement and computational time taken for both training and prediction tasks. The findings reveal notable insights, showcasing that while the Extra Trees Classifier demonstrates commendable accuracy in classifying Fashion MNIST images, the implementation of CPU parallel processing significantly reduces computational time without compromising accuracy levels. This observation underscores the pivotal role of optimizing computational resources for efficient model training and deployment in machine learning applications. The results of this study are very helpful for understanding how to use parallel processing to make machine learning tasks more accurate and more efficient. It also shows how important it is to optimize resources for scalable and effective model development.

OPTYMALIZACJA DRZEWA DECYZYJNEGO OPARTA NA ALGORYTMIE GENETYCZNYM DO WYKRYWANIA DEMENCJI POPRZEZ ANALIZĘ MRI

Dementia is a devastating neurological disorder that affects millions of people globally, causing progressive decline in cognitive function and daily living activities. Early and precise detection of dementia is critical for optimal dementia therapy and management however, the diagnosis of dementia is often challenging due to the complexity of the disease and the wide range of symptoms that patients may exhibit. Machine learning approaches are becoming progressively more prevalent in the realm of image processing, particularly for disease prediction. These algorithms can learn to recognize distinctive characteristics and patterns that are suggestive of specific diseases by analyzing images from multiple medical imaging modalities. This paper aims to develop and optimize a decision tree algorithm for dementia detection using the OASIS dataset, which comprises a large collection of MRI images and associated clinical data. This approach involves using a genetic algorithm to optimize the decision tree model for maximum accuracy and effectiveness. The ultimate goal of the paper is to develop an effective, non-invasive diagnostic tool for early and accurate detection of dementia. The GA-based decision tree, as proposed, exhibits strong performance compared to alternative models, boasting an impressive accuracy rate of 96.67% according to experimental results.

Enhancing Breast Cancer Collagen Segmentation with Improved Swim Transformation Algorithm

Breast cancer is a major health problem around the world, and accurately separating collagen fibers in breast cancer cells is important for both detection and planning treatment. Because it can make the differences between fibers and the background stand out more, the Swim Transformation (SWT) algorithm has shown promise in separating collagen fibers. However, the original SWT algorithm has some flaws, such as being easily affected by noise and artifacts. We suggest a better version of the SWT algorithm in this study to fix these problems and make collagen segmentation in breast cancer tissues better. A new step in the preparation process was added to cut down on noise, and adaptable thresholding was added to make it easier to find collagen fibers. We also add a step called "post-processing" to get rid of any errors and make the segmentation results better. We used a collection of microscopic images of breast cancer tissues in tests to see how well the proposed method worked. For segmentation, we looked at how our better SWT algorithm did compared to the original SWT algorithm and other cutting-edge segmentation methods. Based on our data, the suggested method does a better job in terms of accuracy, sensitivity, and precision. In addition, we add a deep learning model for finding breast cancer that uses Long Short-Term Memory (LSTM) and Bidirectional LSTM models to our work. A set of images of breast cancer is used to teach a deep learning model that can tell from collagen segmentation results whether or not there are dangerous cells. The paper shown an improved SWT algorithm for separating collagen in breast cancer cells. This algorithm fixes the problems with the first one and gets better results for separating collagen. It's possible that the suggested formula will help doctors find and plan better treatments for breast cancer by making it easier to separate collagen fibers more accurately.

Innovative Integration of Convolutional Neural Networks for Enhanced Face Recognition

The face Recognition technique is important in Computer Vision nowadays. The research study focuses on a Face Recognition system that uses deep learning to identify face photos. Face detection and categorization are carried out using various Convolutional neural network (CNN) models using deep learning methods. Prior research has mostly focused on either the ResNet or DenseNet-based CNN models. The current study combines ResNet and DenseNet to create a hybrid model. The suggested work aims to improve efficiency and accuracy. During the simulation's training and testing phases, categories are taken into account. The present study is centered on the Labeled Faces in the Wild (LFW) dataset. The photos go through an initial noise reduction procedure. Picture quality assessment involves considering measures like MSE, PSNR, and SSIM. Once the suggested model has completed training, it produces high-quality images. The suggested system includes the Innovative CNN approach framework, which combines DenseNet with a noise reduction approach, a segmentation mechanism, and a ResNet model based on CNN. Comparative research was performed to assess the precision of several filtered image collections using different convolutional neural network models. The simulation results show that the proposed model had higher performance and accuracy than traditional ResNet and DenseNet models.
  

Image Analysis and Detection of Olive Leaf Diseases Using Recurrent Neural Networks

The widespread adoption of DL has led to a rise in academic interest in image recognition approaches, enabling applications such as automated image classification and the detection of plant diseases. The world's largest producer of olives is Morocco. Plant health might be harmed by illnesses, which therefore affects its development. Numerous illnesses affecting olive leaves specifically target crop growth rate. The objective of this research is to create deep RNNs to identify olive plant illnesses using a collection of leaf images, collected from various sources (Disease note The peacock eye falls on olive trees, Field Guide to Olive Pests, Diseases and Disorders in Australia. Thus, this technique is the best RNN model and is employed in further applications to enhance diagnostic measurements regarding olive leaves and other plant leaves.

Advancing Attendance: A Facial Recognition System Empowered by Deep Learning Techniques

Many studies during the last decade tried to provide an automatic technique for facial recognition and identification challenge especially in security systems. In this study, we suggested two methods for student attendance problem based on image processing and machine learning algorithms. The first method uses Haar cascade classifier with the Local Binary Patterns Histograms (LBPH) model and the second method composed from Histograms of Oriented Gradient (HoG) followed by the Convolutional Neural Network (CNN) model. Both methods take a collection of random student images taken from low quality sources as input. A set of image processing filters are first applied on images to enhance the method of extracting the face boundary. Then, each model will be trained using random images from student dataset. The trained model is tested using testing set. The results showed that the method that employ CNN model with HoG provides high accuracy value of 98.44%. While, the accuracy of LBPH model with Haar Cascade classifier is 95.63%.

The value of a neural network based on multi-scale feature fusion to ultrasound images for the differentiation in thyroid follicular neoplasms

ObjectiveThe objective of this research was to create a deep learning network that utilizes multiscale images for the classification of follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) through preoperative US.MethodsThis retrospective study involved the collection of ultrasound images from 279 patients at two tertiary level hospitals. To address the issue of false positives caused by small nodules, we introduced a multi-rescale fusion network (MRF-Net). Four different deep learning models, namely MobileNet V3, ResNet50, DenseNet121 and MRF-Net, were studied based on the feature information extracted from ultrasound images. The performance of each model was evaluated using various metrics, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, F1 value, receiver operating curve (ROC), area under the curve (AUC), decision curve analysis (DCA), and confusion matrix.ResultsOut of the total nodules examined, 193 were identified as FTA and 86 were confirmed as FTC. Among the deep learning models evaluated, MRF-Net exhibited the highest accuracy and area under the curve (AUC) with values of 85.3% and 84.8%, respectively. Additionally, MRF-Net demonstrated superior sensitivity and specificity compared to other models. Notably, MRF-Net achieved an impressive F1 value of 83.08%. The curve of DCA revealed that MRF-Net consistently outperformed the other models, yielding higher net benefits across various decision thresholds.ConclusionThe utilization of MRF-Net enables more precise discrimination between benign and malignant thyroid follicular tumors utilizing preoperative US.

Minor mining, major influence: Economic implications and policy challenges of artisanal gold mining

This study presents an in-depth analysis of the economic policies and implications surrounding artisanal gold mining, reframing the discussion from geographical specifics to a broader policy and economic context. Utilizing advanced geospatial data from the Landsat image collection through “Google Earth Engine,” the research evaluates the economic impact and policy challenges posed by artisanal gold mining over a period from 2011 to 2021. The approach combines change detection methodologies with machine learning to quantify the economic repercussions of land cover changes due to mining. The analysis reveals significant economic transformations, with an average of 2700 ha of land affected annually, culminating in around 48,000 ha over the decade, highlighting a critical period from 2013 to 2020. Importantly, the study identifies policy gaps, especially in the management of protected areas where approximately 800 ha of mining occurred. The findings underscore the urgent need for policy interventions to mitigate the adverse economic and environmental impacts of unregulated mining. The study contributes to a broader understanding of the economic ramifications of artisanal mining and serves as a call to action for policymakers to address the challenges of sustainable mining practices and economic development.

NutriGreen image dataset: a collection of annotated nutrition, organic, and vegan food products.

In this research, we introduce the NutriGreen dataset, which is a collection of images representing branded food products aimed for training segmentation models for detecting various labels on food packaging. Each image in the dataset comes with three distinct labels: one indicating its nutritional quality using the Nutri-Score, another denoting whether it is vegan or vegetarian origin with the V-label, and a third displaying the EU organic certification (BIO) logo. To create the dataset, we have used semi-automatic annotation pipeline that combines domain expert annotation and automatic annotation using a deep learning model. The dataset comprises a total of 10,472 images. Among these, the Nutri-Score label is distributed across five sub-labels: Nutri-Score grade A with 1,250 images, grade B with 1,107 images, grade C with 867 images, grade D with 1,001 images, and grade E with 967 images. Additionally, there are 870 images featuring the V-Label, 2,328 images showcasing the BIO label, and 3,201 images without before-mentioned labels. Furthermore, we have fine-tuned the YOLOv5 segmentation model to demonstrate the practicality of using these annotated datasets, achieving an impressive accuracy of 94.0%. These promising results indicate that this dataset has significant potential for training innovative systems capable of detecting food labels. Moreover, it can serve as a valuable benchmark dataset for emerging computer vision systems.

DL-EDOF: Novel Multi-Focus Image Data Set and Deep Learning-Based Approach for More Accurate and Specimen-Free Extended Depth of Focus.

Depth of focus (DOF) is defined as the axial range in which the specimen stage moves without losing focus while the imaging apparatus remains stable. It may not be possible to capture an image that includes the entire specimen in focus due to the narrow DOF in microscopic systems. Extended depth of focus (EDOF) is used to overcome this limitation in microscopic systems. Although the researchers have developed so many EDOF microscope approaches, this research field still has some crucial shortcomings such as high computational costs, complexity and execution time, requiring additional equipment, low precise characterization of curves, and edges in images, varying performance depending on the specimen and microscope, using only gray levels of input images to acquire the pixel's focus values. In order to minimize these shortcomings and comprehensively analyze the performance of EDOF approaches, a novel multi-focus image data set is generated, and a deep learning-based EDOF microscope approach is proposed in this study. When compared with the state-of-art EDOF approaches, our study provides various crucial contributions such as the first EDOF approach based on unsupervised deep learning, providing more accurate and specimen-free EDOF, generating a novel multi-focus image data, not requiring any pre- or post-processing technique and acquiring the pixel's focus degrees using deep features. In order to evaluate the effectiveness of the suggested approach, 20 different EDOF approaches are applied to a multi-focus image data set containing 9 image collections (4 synthetic and 5 microscope image collections) in total. Performance analysis metrics with and without requiring a reference image are preferred to identify which EDOF microscope approach can extract more essential details from the multi-focus images for the synthetic and microscope image collections, which are Root Mean Square Error (RMSE), Peak Signal Noise Ratio (PSNR), Universal Quality Index (UQI), Correlation Coefficient (CC), Perception-based Image Quality Evaluator (PIQE), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE), Extension of Universal Quality Index for N Images (UQIN), and Naturalness Image Quality Evaluator (NIQE). Objective and subjective analysis of this study demonstrates that unsupervised deep learning model is more efficient to transmit crucial details from multi-focus images. Moreover, the suggested EDOF microscope approach with highest PSNR, UQI, CC, UQIN and lowest RMSE, PIQE, BRISQUE, NIQE produces higher performance than the state-of-art approaches.

Text-Based Occluded Person Re-identification via Multi-Granularity Contrastive Consistency Learning

Text-based Person Re-identification (T-ReID), which aims at retrieving a specific pedestrian image from a collection of images via text-based information, has received significant attention. However, previous research has overlooked a challenging yet practical form of T-ReID: dealing with image galleries mixed with occluded and inconsistent personal visuals, instead of ideal visuals with a full-body and clear view. Its major challenges lay in the insufficiency of benchmark datasets and the enlarged semantic gap incurred by arbitrary occlusions and modality gap between text description and visual representation of the target person. To alleviate these issues, we first design an Occlusion Generator (OGor) for the automatic generation of artificial occluded images from generic surveillance images. Then, a fine-granularity token selection mechanism is proposed to minimize the negative impact of occlusion for robust feature learning, and a novel multi-granularity contrastive consistency alignment framework is designed to leverage intra-/inter-granularity of visual-text representations for semantic alignment of occluded visuals and query texts. Experimental results demonstrate that our method exhibits superior performance. We believe this work could inspire the community to investigate more dedicated designs for implementing T-ReID in real-world scenarios. The source code is available at https://github.com/littlexinyi/MGCC.

DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling

Many applications use computer vision to detect and count objects in massive image collections. However, automated methods may fail to deliver accurate counts, especially when the task is very difficult or requires a fast response time. For example, during disaster response, aid organizations aim to quickly count damaged buildings in satellite images to plan relief missions, but pre-trained building and damage detectors often perform poorly due to domain shifts. In such cases, there is a need for human-in-the-loop approaches to accurately count with minimal human effort. We propose DISCount -- a detector-based importance sampling framework for counting in large image collections. DISCount uses an imperfect detector and human screening to estimate low-variance unbiased counts. We propose techniques for counting over multiple spatial or temporal regions using a small amount of screening and estimate confidence intervals. This enables end-users to stop screening when estimates are sufficiently accurate, which is often the goal in real-world applications. We demonstrate our method with two applications: counting birds in radar imagery to understand responses to climate change, and counting damaged buildings in satellite imagery for damage assessment in regions struck by a natural disaster. On the technical side we develop variance reduction techniques based on control variates and prove the (conditional) unbiasedness of the estimators. DISCount leads to a 9-12x reduction in the labeling costs to obtain the same error rates compared to naive screening for tasks we consider, and surpasses alternative covariate-based screening approaches.

Directed Diffusion: Direct Control of Object Placement through Attention Guidance

Text-guided diffusion models such as DALLE-2, Imagen, and Stable Diffusion are able to generate an effectively endless variety of images given only a short text prompt describing the desired image content. In many cases the images are of very high quality. However, these models often struggle to compose scenes containing several key objects such as characters in specified positional relationships. The missing capability to ``direct'' the placement of characters and objects both within and across images is crucial in storytelling, as recognized in the literature on film and animation theory. In this work, we take a particularly straightforward approach to providing the needed direction. Drawing on the observation that the cross-attention maps for prompt words reflect the spatial layout of objects denoted by those words, we introduce an optimization objective that produces ``activation'' at desired positions in these cross-attention maps. The resulting approach is a step toward generalizing the applicability of text-guided diffusion models beyond single images to collections of related images, as in storybooks. Directed Diffusion provides easy high-level positional control over multiple objects, while making use of an existing pre-trained model and maintaining a coherent blend between the positioned objects and the background. Moreover, it requires only a few lines to implement.

Tell Me What Is Good about This Property: Leveraging Reviews for Segment-Personalized Image Collection Summarization

Image collection summarization techniques aim to present a compact representation of an image gallery through a carefully selected subset of images that captures its semantic content. When it comes to web content, however, the ideal selection can vary based on the user's specific intentions and preferences. This is particularly relevant at Booking.com, where presenting properties and their visual summaries that align with users' expectations is crucial. To address this challenge, in this work, we consider user intentions in the summarization of property visuals by analyzing property reviews and extracting the most significant aspects mentioned by users. By incorporating the insights from reviews in our visual summaries, we enhance the summaries by presenting the relevant content to a user. Moreover, we achieve it without the need for costly annotations. Our experiments, including human perceptual studies, demonstrate the superiority of our cross-modal approach, which we coin as CrossSummarizer over the no-personalization and image-based clustering baselines.